Applied Sciences doi: 10.3390/app16115352

Authors: Ahmed M. Ahmed Jeffrey Shragge Ilya Tsvankin

Real-time seismic reservoir monitoring of geologic reservoirs requires both large-scale data management and efficient computational workflows. Addressing these challenges is facilitated by developing techniques capable of selectively capturing critical geologic features, thereby increasing computational efficiency and reducing data storage requirements. This paper proposes a double-compression framework integrating Haar wavelet transforms with machine learning (ML) for efficient multiparameter seismic inversion. First, Haar wavelet compression significantly reduces the dimensionality of the input elastic models, preserving essential geologic structures while limiting data volumes. Next, a convolutional neural network with the long short-term memory (CNN-LSTM) architecture, including dual encoders and multi-decoders, compresses seismic data into a latent space to generate a multi-scale P-wave velocity estimate. By leveraging transfer learning to speed up convergence and enhance prediction accuracy, we fine-tune the latent representation to estimate the P-to-S-wave velocity ratio and acoustic impedance at multiple resolution scales. Tests on the synthetic CO2-injection Kimberlina model show that wavelet-based compression—including detuning large-scale trends—minimizes artifacts in simulated wavefields and accelerates neural-network training. The results demonstrate that combining wavelet-based pre-compression for reservoir models with data-driven latent encodings for seismic data achieves high compression ratios, reduces computational costs, and maintains the fidelity of subsurface imaging. Compared with a redundant-decimation baseline, the proposed framework reduces network training time by approximately 70% and GPU memory usage by 33–73%, achieves a wavefield energy loss below 0.1% at a 16:1 model-dimension reduction, and produces multi-resolution predictions of VP, VP/VS, and acoustic impedance with normalized errors below 0.04 across all six wavelet decomposition levels. Thus, the double-compression framework enables robust and scalable seismic monitoring of elastic reservoir parameters.

Vibration doi: 10.3390/vibration9020038

Authors: Junbiao Xie Yuying Jiang Chen Wang Jingcheng Dai Yiming Yu Chenglong Pan

To address the vibration reduction problem of the six-degrees of freedom(6-DOF) half-vehicle model and to improve ride comfort and handling stability, a piezoelectric stack actuator based on the inverse piezoelectric effect was introduced. A 6-DOF half-vehicle dynamic model coupling the cab, body, and wheels was established based on the Lagrange equation. Based on this model, a vertical-pitch dual sliding surface RBF neural network sliding mode control strategy was proposed, with two independent RBF neural networks designed to separately approximate, online, the comprehensive uncertainties in the vertical and pitch channels associated with unmodeled dynamics, external disturbances, and modeling simplifications. The variable-speed reaching law (dsat) function was used to design the sliding mode reaching law, balancing sliding surface convergence speed and vibration suppression. Six indicators, including vertical acceleration of the cab and vertical acceleration of the vehicle body, were selected as performance evaluation metrics to establish the fitness function. Combined with a genetic algorithm, the dual sliding surface coefficients, RBF network parameters, adaptive update rates, and variable-speed reaching law parameters were globally optimized. The vibration reduction effects of four schemes—passive control, traditional sliding mode control, RBF sliding mode control, and genetic algorithm optimized RBF dual-sliding-mode control—were compared and analyzed. Simulation results show that the genetic algorithm optimized RBF dual-sliding-mode control achieves improved vibration suppression in several key ride-comfort-related indices and provides better overall coordination among ride comfort, suspension working space, and tire dynamic deflection. The research results validate the effectiveness of this method and provide a new solution for addressing vehicle vibration reduction problems.

Computers doi: 10.3390/computers15060342

Authors: Araceli Olmos-Vallejo Lisbeth Rodríguez-Mazahua Isaac Machorro-Cano José Antonio Palet-Guzmán Giner Alor-Hernández Jair Cervantes José Luis Sánchez-Cervantes

The use of open-ended survey questions for data collection has increased significantly across various areas, as has the application of machine learning (ML) and natural language processing (NLP) techniques to analyze respondents’ opinions. In this study, we conducted a scoping review of 79 studies that analyze open-ended answers given in surveys. We structured our review around six main criteria: application of supervised learning, unsupervised learning, Supervised Descriptive Rule Discovery (SDRD), open-ended questions, NLP, and opinion comparison. This approach allowed us to identify the most used tasks, algorithms, and technologies in ML and NLP, revealing areas of opportunity and the main future challenges. We based our review on the methodological framework of Arksey and O’Malley and adapted PRISMA for reporting systematic reviews. Our findings suggest that most studies addressing surveys with open-ended questions were published in 2020 and 2022, predominantly focusing on research and health domains.

Entropy doi: 10.3390/e28060595

Authors: Zhouheng Wu Tao Zhou Jianyong Fan Ruimin Zhang Zhigang Li Kang Hu

Feature selection (FS) plays a crucial role in high-dimensional data analysis by improving model performance and reducing computational complexity. However, existing metaheuristic-based FS methods often suffer from insufficient population diversity, premature convergence, and limited capability to escape local optima, which substantially constrains their effectiveness in complex search spaces. To address these challenges, this paper proposes a novel Improved Sand Cat Swarm Optimization algorithm with multi-strategy fusion (ISCSO) for feature selection. The proposed method introduces a hybrid initialization mechanism based on the Hénon chaotic map and lens imaging reverse learning to enhance population diversity. A golden sine-based phase adjustment strategy is further incorporated to achieve a more effective balance between global exploration and local exploitation. In addition, a nonlinear adaptive weight mechanism is designed to dynamically regulate the search process, while a simulated annealing-based acceptance criterion is integrated to improve the ability to escape local optima. Comprehensive experiments are conducted on the CEC2017 benchmark suite and 18 real-world datasets from the UCI repository. The results demonstrate that ISCSO achieves superior performance over state-of-the-art algorithms, obtaining the optimal results on 82.76% of benchmark functions. In feature selection tasks, ISCSO achieves the optimal average fitness on 94.44% of datasets, reduces feature dimensionality significantly, and consistently improves classification accuracy. These findings indicate that ISCSO provides a competitive and reliable solution for high-dimensional feature selection and complex optimization problems.

Smart Cities doi: 10.3390/smartcities9060093

Authors: Nikola Zogović Miloš D. Jevtić Dragana Bajić Goran Dimić

Temperature has a significant impact on the operation and performance of electronic systems. Conventional approaches focus on stabilizing electronic systems to maintain functionality under unfavorable thermal conditions, typically at the expense of increased consumption. This paper adopts a multi-objective approach to identify the Pareto-optimal (PO) trade-off across varying temperatures between functionality and consumption of low-power radio transceivers used in the Internet of Things (IoT) and wireless sensor networks. Building upon the established two-segment PO trade-off controlled by supply voltage and output power settings, between engaged and achieved transmission power, parameters directly associated with energy consumption and transmission quality, we analyze the influence of temperature on the Pareto front. We find that decreasing the temperature improves both engaged power and achieved transmission power simultaneously. Therefore, we propose a novel Pareto-optimal temperature-opportunistic wireless communication approach that exploits temperature variability by selecting favorable temperature conditions for transmission. We also identify the spatio-temporal potential of temperature variations across a four-dimensional network deployment space, particularly in temperature-dynamic urban environments of smart city infrastructure supporting massive IoT. Experiments on a modern Texas Instruments CC1200 transceiver confirm that the power savings of approx 30% and nearly 450 times increase in achieved transmission power are attainable for a temperature difference of 60 °C, corresponding to realistic conditions between the ambient air and a black-painted surface.

Processes doi: 10.3390/pr14111737

Authors: Jie Liu Yican Wang Huimin Yu

Lipopeptide biosurfactants and petroleum sulphonates (PSs) have complementary molecular structures that can achieve ultralow interfacial tension (IFT), which is considered the primary mechanism for enhanced oil recovery (EOR). In this study, the phase behavior of lipopeptide compounded with PS/crude oil/water was investigated, which revealed that lipopeptide addition led to the formation of Winsor III middle-phase microemulsion. The synergistic mechanism of ultralow IFT and microemulsion formation enables the lipopeptide-compounded system (LASP) to achieve superior oil displacement efficiency compared with the regular alkaline/surfactant/polymer (ASP) flooding system. Core flooding results proved that under the same conditions, the LASP system increased oil recovery by 10.58% relative to the ASP system. Furthermore, when the ASP system could no longer improve recovery, switching to the LASP system provided an additional 9.55% oil recovery rate. Moreover, the LASP system exhibited superior wettability, interfacial activity, and anti-adsorption properties. These findings highlight the potential of lipopeptide biosurfactants as high-performance, environmentally friendly alternatives to synthetic surfactants in EOR processes.

Applied Sciences doi: 10.3390/app16115351

Authors: Marui Han Zhiping Zeng Zijie Li Peicheng Li Guangzhao Peng Weidong Wang Abdulmumin Ahmed Shuaibu

The wheel–rail impact effect is prominent in the low vibration track (LVT) in the curved sections of heavy-haul railways, where fastener failure and the support block hanging void are prone to occurring. To investigate the impact of these issues on the dynamic characteristics of the vehicle–track coupled system, this study establishes a coupled dynamics model of a heavy-haul train and LVT, taking into account the topological relationships of vehicle components, multipoint wheel–rail contact, and track irregularities. Comparative analyses are conducted to evaluate the effects of the location, quantity, and failure degree of fastener failure and support block hanging voids on running safety and stability. The results show that (1) compared to the normal condition, fastener failure and support block hanging voids lead to varying degrees of increases in response indicators, thereby intensifying the wheel–rail impact; (2) bilateral failure exhibits more pronounced dynamic responses than unilateral failure, and when the number of failed fasteners or hanging voids exceeds one, the maximum wheel load reduction rate increases significantly; (3) as the gap of the hanging void increases, the dynamic response also increases, and when the gap reaches approximately 3 mm, the support block can be considered fully suspended; and (4) comprehensive analysis indicates that fastener failure poses a greater threat to running safety than support block hanging voids and thus warrants greater attention in practical engineering applications. This study provides theoretical support for the maintenance and repair of heavy-haul railways.

Buildings doi: 10.3390/buildings16112134

Authors: Jiaqi Wang Ye Min Jing Tan Zijing Tan

Urban street canyons exert a critical influence on local microclimates; however, the dynamics of mixed convective airflow under unsteady wind and thermal forcing remain poorly quantified. This study systematically investigates the spatiotemporal characteristics of airflow within symmetric and asymmetric street canyons through integrated long-term field measurements and complementary CFD simulations. Field data collected over 120 monitoring days at the Weishui Campus of Chang’an University were analyzed using the Levenberg–Marquardt nonlinear curve-fitting algorithm. The analysis demonstrates that sine functions accurately represent diurnal surface temperature variations during consecutive clear sky periods, whereas polynomial functions of varying orders are required to characterize meteorologically complex episodes, including cold-wave cooling and seasonal transitions. Ambient wind patterns outside the canyon were further classified into two characteristic variation modes: stepwise and gradual. Complementary unsteady RANS simulations, with wall boundary conditions derived directly from the fitted field data, reveal that canyon geometry and meteorological forcing jointly govern the evolution of airflow structures and thermal distributions across seasons. In the symmetric canyon, the flow transitions from complex multi-vortex activity in spring and summer to a more stable regime in autumn, with two well-defined counter-rotating vortices emerging during winter cold-wave events. In the asymmetric canyon, strong summer solar heating sustains a dominant leeward vortex with a strengthening secondary structure, whereas winter cold wave intrusion generates a hierarchically nested vortex system in which secondary and tertiary vortices progressively develop and detach. By coupling empirical surface temperature functions with CFD boundary conditions, this study advances the precision of predictive microclimate models and provides an evidence-based framework for optimizing street canyon geometry to enhance ventilation performance, energy efficiency, and outdoor thermal comfort.

Mathematics doi: 10.3390/math14111853

Authors: Xinye Liu Hongfeng Wang Chenxi Tang

Against the backdrop of mass customization, research interest in hybrid flow shop scheduling for standard and customized part production has been on the rise. However, most extant studies focus on single-shop scheduling optimization, and the inter-shop coordination mechanism for heterogeneous multi-shop systems remains underexplored. This paper investigates a heterogeneous hybrid flow shop scheduling problem featuring a distributed flow shop for standardized parts and a flexible job shop for customized parts, with the dual objectives of minimizing makespan and total cost. For this problem with the core complexity of heterogeneous cross-shop production reliance and conflicting dual-objective optimization, we propose a multi-objective grey wolf optimizer (MOGWO) combined with problem-specific local search strategies. Computational experiments on a set of test instances are carried out to evaluate the MOGWO’s performance, which is further compared with four classic multi-objective evolutionary algorithms of analogous algorithmic frameworks. Experimental results confirm that the proposed algorithm achieves superior solution quality and convergence efficiency for the multi-objective heterogeneous hybrid flow shop scheduling problem under study.

Applied Sciences doi: 10.3390/app16115350

Authors: Gaspar Cano José Manuel Ortega-Candel Francisco José Mora-Gimeno Lucía Arnau-Muñoz Higinio Mora

In the era of cloud computing and serverless architectures, the security of applications and services has become a critical challenge. Serverless computing, often referred to as function as a service (FaaS), is a cloud computing model that allows developers to build and run applications without the need to manage traditional server infrastructure. Serverless architectures have gained popularity in cloud computing due to their flexibility and ability to scale automatically based on demand. These architectures are based on executing functions without the need to manage the underlying infrastructure. Denial of wallet (DoW) attacks refer to a type of cyberattack that aims to exploit and exhaust the financial resources of an organization by triggering excessive costs or charges within their cloud or serverless computing environment, exploiting characteristics such as the pay-as-you-go model, auto-scaling, limited control, and cost amplification. This research aims to assess existing methods for detecting distributed denial of service (DDoS) attacks and extend their application to detect denial of wallet (DoW) threats, leveraging a dataset tailored to serverless architectures. We investigate various strategies and techniques that employ entropy, machine learning and deep learning algorithms to enable early detection of DDoS and DoW attacks in serverless environments. This research provides insights into the options that are available for detecting DoW attacks in serverless environments, allowing security professionals and developers to make decisions on the most appropriate solutions to protect their applications and cloud services.

Symmetry doi: 10.3390/sym18060912

Authors: Hassan Ugail Newton Howard

Useful quantum neural networks should not merely explore large Hilbert spaces but should organise their expressive capacity according to the symmetries of the learning problem. We introduce symmetry-organised complexity as an ansatz-level, representation-theoretic trajectory diagnostic for quantum neural networks. The diagnostic combines symmetry-sector organisation, cross-irreducible representation organised complexity, and symmetry metastability into a composite index, which is then multiplied by a compliance factor that penalises apparent complexity arising from symmetry violation. This compliance factor is defined at the level of the implemented trainable generators rather than as a representation-independent channel metric. The representation-theoretic basis of the construction is that, for an exactly equivariant network, the effective trainable operators lie in the commutant of the group action and are controlled by multiplicity dimensions rather than by the full Hilbert-space dimension. We show that joint sector collapse and state freezing force the index to vanish under an explicit multiplicity–purity condition and that networks with identical qubit and parameter counts can have different values of the index. Two analytically tractable four-qubit examples with excitation number and total spin symmetry illustrate how the diagnostic separates sector-collapsed, symmetry-organised, and symmetry-breaking behaviour. A controlled U(1)-compatible teacher–student classification task further shows that, in this validation setting, the ordering of the composite index across equivariant, hybrid, and non-equivariant ansatze agrees with the ordering of generalisation accuracy. The framework is most informative when the relevant symmetry of the learning problem is known.

Mathematics doi: 10.3390/math14111852

Authors: Kun Shuai Qian Qian

Liquidity distress can propagate through trade credit, contractual obligations, and financing dependencies in production networks. Existing network-contagion studies largely ask how distress spreads, but say much less about how scarce stabilization resources should be allocated across heterogeneous firms. This paper develops a heterogeneous network model in which firms are divided into structurally central large firms and small- and medium-sized enterprises (SMEs) with lower recovery capacity in the benchmark setting. The model incorporates recurrent healthy–distressed transitions, asymmetric contagion across firm types and network directions, decaying support stocks, preventive and curative support channels, and expectation-driven feedback linking aggregate distress to effective contagion and recovery probabilities. A reduced two-block approximation is used to characterize a local persistence threshold defined by the spectral radius of the Jacobian at the low-distress equilibrium. The analysis shows that targeted support need not dominate uniform support: its value depends on whether allocation priorities match the firm groups or network positions that generate the largest marginal reduction in persistent distress. Simulations on directed scale-free networks show that policy scale mainly determines peak containment, whereas allocation architecture primarily affects post-peak adjustment and long-run distress. Recovery-enhancing support plays a larger role in post-peak stabilization, and combined preventive–curative support yields stronger resilience than either channel alone. The framework provides a tractable basis for analyzing stabilization rules under limited support capacity in heterogeneous production networks.

Processes doi: 10.3390/pr14111738

Authors: Yisong Ni Yu Jiang Yuan Zong Sixian Zheng

Poly(trimethylene ether) glycol (PO3G), a bio-based polyether polyol with excellent flexibility and superior hydrolytic stability, has emerged as a critical raw material for the preparation of high-performance polymer materials. This work optimized the sulfuric acid-catalyzed polymerization process and assessed the feasibility of using p-toluenesulfonic acid (PTSA) as an alternative catalyst. A parametric study was conducted to establish a reliable operating window for the sulfuric acid system. DFT calculations demonstrated that the driving force for chain growth decreases with increasing chain length, that recombination between chains of significantly different lengths is more favorable than between chains of equal length, and that the formation of disulfate esters is thermodynamically more favorable. Although PTSA required a higher catalyst loading, the resulting polymer had a markedly lower yellowness index. Prolonged reaction times lead to a molecular weight plateau, especially at high PTSA concentrations, while the yellowness index continues to increase after reaching the plateau. 1H NMR analysis indicated the formation of benzenesulfonate monoester intermediates during PTSA catalysis, suggesting a potentially milder pathway and possibly fewer side reactions compared to the sulfuric acid system. This paper provides theoretical and experimental foundations for the green, efficient synthesis of PO3G and the catalyst optimization for analogous bio-based polyether polyols.

Information doi: 10.3390/info17060528

Authors: Marwa Abdellah Aya Hesham Ahmad Salah Gamal M. Behery

Federated Learning (FL) is a machine learning technique that preserves data privacy and security by training models directly on decentralized edge network devices. This generates substantial communication overhead due to the repeated exchange of model updates across numerous edge network devices. Quantization has tackled this challenge by reducing communication overhead and computational costs by quantizing model updates. Although selecting the most suitable quantization level to balance communication efficiency and model accuracy is challenging, failing to achieve this balance results in excessive compression, leading to accuracy degradation due to the lossy nature of the quantization technique. This challenge was tackled in this paper via a Quantile-based lossless compression method named Pcodec, which implements lossless compression in the FL context. Pcodec is a Quantile-based lossless compression algorithm designed for numerical data that utilizes mode identification with delta encoding and binning, where binning groups similar values into entropy-coded bins and stores the exact offset within each bin, thus achieving high compression ratios and efficient processing speed. Using MNIST and CIFAR-10 datasets and models such as CNN and ResNet18, we demonstrate that Pcodec achieves up to 58.19% size reduction with no accuracy loss compared to standard quantization methods. The experiments showed that the proposed Quantile-based compression approach in FL reduces up to 2.81× the communication overhead between each server and edge network device while maintaining the accuracy. In comparison to quantization, the Quantile approach reduced the communication overhead by 2.74×, tackling the main challenge of FL context by reducing communication overhead with a remarkably high compression ratio while maintaining the model’s accuracy.

Processes doi: 10.3390/pr14111736

Authors: Xin Xu Wuyang Yang Xinjian Wei Kai Zhang Weisheng Wang Xiangyang Zhang Haishan Li

Three-dimensional geological structural modelling provides the geometric framework for sub-surface exploration and development. However, conventional workflows, driven primarily by seismic interpretation, often lack explicit constraints from expert knowledge and are difficult to update when interpretations evolve. In particular, the conventional surface-based workflow follows a sequential pipeline—from seismic interpretation through manual intersection editing to surface generation and pillar gridding—in which geological knowledge is embedded only implicitly through operator-dependent parameter tuning, making knowledge transfer and model reproducibility difficult. This study proposes an intelligent modelling methodology guided by a geological structure knowledge graph. The method includes: (i) a three-tier knowledge architecture (TKA) that formalises domain knowledge in entity, relationship and inference layers using RDF/OWL; (ii) a knowledge-driven intersection line generation algorithm (KILGA) coupled with a hierarchical adaptive mesh refinement scheme based on a posteriori error estimation (HAMR-APEE) to integrate geological constraints and mitigate boundary aliasing; and (iii) a bidirectional linkage mechanism between the knowledge graph and 3D models to support incremental updates following knowledge revision. The approach is validated in three petroliferous basins in China (Ordos, Qaidam and Sichuan), representing micro-amplitude, thrust-nappe and deep complex structural styles. Compared with a conventional surface-based workflow, the proposed method reduces modelling RMSE from 15–20 m to 5–8 m, improves geological reasonableness from ~85% to >95%, and shortens modelling cycles from months to weeks. These results demonstrate that explicit integration of formalised geological knowledge into the modelling pipeline can substantially enhance both accuracy and efficiency across a range of structural settings.

Fractal and Fractional doi: 10.3390/fractalfract10060361

Authors: Ghaziyah Alsahli Muhammad Nazam Nura Alotaibi

This manuscript aims at the introduction of interpolative FG-contractions and their application to derive fixed-point results. As an application, one of the obtained results is used to analyze the fractional-order Aizawa model. Moreover, we introduce a Hutchinson–Barnsley operator defined in terms of interpolative FG-contractions and a related iterated function system to prove the existence of a unique fractal. The theoretical findings are supported with illustrative examples and graphical demonstrations. This manuscript also sheds light on a theoretical framework for analyzing material decomposition and recycling processes.

Mathematics doi: 10.3390/math14111851

Authors: Yifan Nie Qiaoxin Li

This paper investigates the temporal evolution of high-speed compressible fluids governed by the two-dimensional Euler equations in irregular flow fields using the Fourier Neural Operator (FNO). We reconstruct the irregular flow field point set into sequential format compatible with FNO input requirements, and then embed temporal bundling technique within a recurrent neural network (RNN) for multi-step prediction. We further employ a composite loss function to balance errors across different physical quantities. Experiments are conducted on three different types of irregular flow fields, including orthogonal and non-orthogonal grid configurations. Then we comprehensively analyze the physical component loss curves, flow field visualizations, and physical profiles. On non-orthogonal grids, our method consistently achieves improvements in both computational efficiency and error compared to other baseline models. Results demonstrate that our approach achieves high accuracy, as evidenced by maximum relative L2 errors of (0.75%,0.56%,0.35%) for (p,T,∥u∥) respectively (where p, T, and ∥u∥ denote pressure, temperature, and velocity magnitude), and offers substantial improvements in computational efficiency over traditional numerical methods. Within this data-driven context, the method accurately and efficiently simulates the temporal evolution of high-speed compressible flows in irregular domains.

Mathematics doi: 10.3390/math14111850

Authors: Haichao Yang

This study formulates a corporate abatement decision problem under carbon price uncertainty as a continuous-time stochastic control model. To this end, the carbon price is modeled as a geometric Brownian motion, while abatement capacity is accumulated through costly effort and depreciates over time. Specifically, the firm chooses its abatement effort to maximize expected discounted profits while accounting for allowance purchasing costs, compliance-related penalties, abatement costs, and potential revenues from surplus allowances. The paper contributes by integrating stochastic carbon prices, endogenous abatement-capacity accumulation, allowance-shortage/allowance-surplus asymmetry, and surplus allowance monetization into a unified corporate abatement framework. Applying the dynamic programming principle, the associated Hamilton–Jacobi–Bellman equation is derived, and the bounded optimal abatement effort is characterized in feedback form. Since the resulting nonlinear HJB equation generally does not admit a closed-form solution, a finite-difference scheme with damped policy iteration is used for numerical analysis. The results show that optimal abatement effort is strongly state-dependent. Higher carbon prices strengthen abatement incentives in the allowance-shortage region, whereas effort declines sharply after reaching allowance neutrality if surplus allowances cannot be monetized. Moreover, partial monetization of surplus allowances significantly increases abatement effort in the surplus region and can shift firms’ behavior from passive compliance to active low-carbon investment. Overall, these findings suggest that surplus allowance monetization plays an important role in sustaining firms’ abatement incentives under carbon price uncertainty.

Network doi: 10.3390/network6020034

Authors: Sofia Polymeni Georgios Spanos Stefanos Georgiadis Anastasios Pechlivanidis Dimitris Tsiktsiris Evangelos Athanasakis Konstantinos Votis Dimitrios Tzovaras Georgios Kormentzas

Weather forecasting, given the ever-increasing occurrence of climate change-induced events, has been widely introduced as a method to offer accurate and timely forecasts for proactive measures and risk mitigation. Artificial intelligence of things (AIoT) offers promising solutions for short-term weather forecasting, contributing to the advancement of sustainable and efficient weather monitoring technologies. This work presents everWeather_2.0, a significantly enhanced low-cost and self-powered AIoT-based weather forecasting station, which addresses key challenges in power consumption, user engagement and forecasting accuracy. The proposed end-to-end Cloud-Edge-IoT (CEI) proof-of-concept solution improves upon its predecessor by combining a more robust renewable energy subsystem for complete power autonomy with a series of lightweight, adaptive statistical models for on-device forecasting and an integrated display for on-site user engagement. Deployed in a real-world scenario, the station demonstrated seamless operation and high short-term forecasting accuracy for the thermodynamic variables during the pilot deployment period, with model errors observed as low as 2% for 30 min forecasts to 4.3% for 120 min intervals, validating its applicability in real-time and continuous physical weather monitoring. While wind speed and rainfall were monitored, they were excluded from the current accuracy metrics due to their high volatility and the insufficient number of events recorded during the pilot period to ensure reliable modeling.

Applied Sciences doi: 10.3390/app16115349

Authors: Rendra Hakim Hafyan Vinod Kumar Sunil K. Maity Jhuma Sadhukhan Siddharth Gadkari

Food waste (FW) is a major global challenge with significant economic and environmental costs, yet its nutrient-rich composition also offers an opportunity for valorization into high-value biochemicals and biofuels within a circular bioeconomy. Effective FW management requires systematic frameworks that balance environmental performance, economic returns, and social acceptance, a challenge that is particularly difficult in developing countries where technical, financial, and participation barriers persist. This review proposes a strategic, step-by-step approach to enhance current FW management through the objective integration of biorefinery pathways producing biochemicals and biofuels products. Both biochemical and thermochemical conversion routes are evaluated against industrial yield benchmarks, market value, and end-use specifications to identify the products and processes most capable of enhancing sustainability. The review further presents a framework for multi-objective optimization (MOO) that simultaneously addresses economic, environmental, and social objectives, and for incorporating decision-maker preferences into the selection of optimum solutions. By coupling sustainability assessment with structured decision support, this review provides practical guidance for selecting FW management strategies that are economically viable, environmentally sound, and socially acceptable.

Sci doi: 10.3390/sci8060122

Authors: Joaquim Jose Carvalho Proença Carlos Enrique Bermudes Mendoza Rosita Elvira Alcantara Poma Nelly Gisella Quispe Quispe Carmen Ramos Vera

Artificial intelligence (AI) has become a central driver of transformation in innovation management, reshaping how organizations design strategies, develop offerings, and generate knowledge. This study examines how innovation management has evolved from the pre-ChatGPT era—characterized by analytics, automation, and decision support—to the post-ChatGPT period, marked by the widespread adoption of generative AI (GenAI) and human–AI collaboration. Using a structured literature review of Scopus-indexed studies published between 2020 and 2025, the paper identifies the following six dominant thematic dimensions of AI-enabled innovation management: strategic and business model innovation, product and service innovation, sustainability-oriented innovation, organizational agility and capabilities, human-centric innovation, and knowledge, learning, and research. The findings reveal a conceptual shift from efficiency-driven applications toward more creative, strategic, and collaborative uses of AI, with generative models acting as co-creators rather than mere analytical tools. The study contributes by synthesizing the fragmented literature into an integrative framework that captures this transition and by highlighting emerging research gaps, particularly in sustainability and human-centered innovation. Practical implications for managers and policymakers are discussed.

Applied Sciences doi: 10.3390/app16115345

Authors: Ziemowit Bańkosz Pengfei Jin Anna Węgrzyn Katarzyna Węgrzyn Sławomir Winiarski

(1) Background: The aim of this study was to determine the biomechanical role of plantar pressure distribution in generating racket velocity during the topspin forehand in table tennis players, with particular emphasis on its relationship with stroke kinematics and performance level. (2) Methods: The study involved 14 male table tennis players divided into Elite and Sub-Elite athletes. Each participant performed a topspin forehand stroke. The study employed a biomechanical analysis combining inertial motion capture and plantar pressure measurement to assess the relationship between lower limb loading and racket velocity during the topspin forehand. (3) Results: The statistical evidence supports the subsequent phase-by-phase comparisons, indicating that the Elite (EL) and Sub-Elite players (SE) differ in execution of the topspin forehand, and the Elite group achieved significantly higher racket speed values in all phases (e.g., in hitting phase: SE-13.8 m/s, EL-15.6 m/s, p ≤ 0.001, d = 1.0; in post-impact follow-through phase: SE-13.8 m/s, El-16.1 m/s, ≤0.001, d = 1.3) and exhibited also a different pattern of foot loading. An analysis of the correlation between the plantar pressure and velocity of the racket in individual events revealed numerous significant correlations. (4) Conclusions: The study identified numerous correlations between the maximum plantar pressure and the maximum racket speed in the individual phases of the stroke. This demonstrates the active involvement of the feet throughout the entire kinematic chain of the topspin forehand stroke and highlights the importance of foot coordination for the outcome of this stroke, namely the speed of the racket-wielding arm.

Processes doi: 10.3390/pr14111735

Authors: Anastasia Rapeyko Antonio Eduardo Palomares Urbano Díaz Michael Renz

The increasing share of renewable energy, such as solar and wind energy, in the energy mix implies a demand for sustainable energy storage systems for the mitigation of the intermittency of these energy sources. One option, therefore, is stationary batteries based on abundant sodium, stored in hard carbon (HC) anodes. In this work, following the sustainable by design principle, HCs were synthesized from cotton-based textile waste using three different thermochemical routes: hydrothermal carbonization (HTC) followed by pyrolysis under nitrogen atmosphere (HC-250-N), HTC followed by pyrolysis under a water vapor stream (HC-250-W), and direct pyrolysis (HC-direct-N). The impact of the synthesis method on the physicochemical properties and electrochemical performance of the HCs was thoroughly investigated. X-ray diffraction, Raman spectroscopy, electron microscopy, and gas adsorption analyses revealed that the HTC pre-treatment significantly enhanced the carbon content, microporosity, and degree of structural graphitic order. HC-250-N exhibited the highest graphitic character and more uniform microstructure, while HC-250-W showed the largest specific surface area and broader micropore distribution. Electrochemical evaluation in sodium-ion half-cells indicated that HC-250-N delivered the most balanced performance, with a reversible capacity of 335 mAh g−1 and good cycling stability. These findings confirm the potential of textile waste-derived HCs as promising and sustainable anode materials for sodium-ion batteries and highlight the importance of tailoring synthesis parameters—such as HTC treatment and pyrolysis conditions—to optimize their structural and electrochemical properties.

AI doi: 10.3390/ai7060194

Authors: Roberto C. Sotero Jose M. Sanchez-Bornot

The Transformer architecture drives modern Artificial Intelligence (AI), yet the physical principles that may constrain self-attention training remain poorly characterized. We develop a thermodynamic framework for attention training, drawing on the established Boltzmann correspondence between softmax attention and equilibrium statistical mechanics, and we propose a First Law analogue that decomposes the training energy budget into a heat term (the entropic cost of ordering attention) and a work term (the gain in mutual information about the target). From this framework we derive a Landauer-type bound on learning, which states that the loss reduction during training is bounded below by the entropic cost of structuring attention against thermal noise. The bound is satisfied across all configurations tested: 625 grid points spanning three datasets on a compact Vision Transformer trained from scratch (MNIST, CIFAR-10, and OrganAMNIST), and ten temperatures on a pretrained ViT-Small fine-tuned on Food-101. Reusing the same physical principles at inference time, we show that the thermodynamic work performed by each input patch provides a quantitative, energy-based measure of feature importance that outperforms standard attention weights and Integrated Gradients on ImageNet across pretrained ViT-Small, ViT-Base, and ViT-Large (22M to 304M parameters). The result is an integrated diagnostic framework that links phase structure, training-time bounds, and inference-time attribution within a single empirically falsifiable thermodynamic apparatus.

Entropy doi: 10.3390/e28060594

Authors: Leonardo Ermann Alexei D. Chepelianskii Dima L. Shepelyansky

The Boltzmann–Loschmidt dispute of 1876 questioned the possibility of a statistical irreversible description by time-reversible classical equations of motion of atoms. Here we show analytically and numerically that the quantum chaos diffusion of cold atoms, or ions, in a harmonic trap and pulsed optical lattice can be inverted back in time with up to 100% efficiency. This is in sharp contrast to classical evolution, where exponentially small errors break time reversibility. We argue that the existing experimental skills allow highlighting the Boltzmann–Loschmidt dispute from a quantum perspective.

Symmetry doi: 10.3390/sym18060911

Authors: Slim Dhahri Essia Ben Alaia Sahar Almashaan Hatem Alwardi Omar Naifar

This paper studies observer-based stabilization of a normalized incommensurate fractional-order discrete-time SI benchmark model for computer-virus propagation. The model is formulated with Caputo-like fractional-difference operators and allows the susceptible and infected compartments to have different memory orders. In contrast with a predictive malware-forecasting model, the proposed system is explicitly treated as a dimensionless benchmark for qualitative analysis and control design. To clarify how the benchmark can be connected to empirical cybersecurity data, the revised formulation includes a calibration and fractional-order selection procedure based on normalized infection telemetry, admissible parameter sets, and loss minimization. The incommensurate orders are therefore interpreted as identifiable modeling parameters, not as arbitrary constants. The plant, observer, and control laws are formulated on the integer update grid, and the memory terms are implemented through the equivalent Volterra-type convolution representation. A nonlinear Luenberger-type observer is proposed under infected-state measurements, which is justified as a detectability-based cyber-monitoring configuration rather than a full observability assumption. The observer gain design, the full-state feedback design, and the observer-based output-feedback design are derived from first-order linearized incommensurate fractional-order models. The resulting criteria are expressed through characteristic-root conditions associated with linear incommensurate Caputo-type fractional-order difference systems. The scope of the theoretical claims is made explicit: the results provide local linearized-design guarantees and do not establish global or semi-global nonlinear stabilization. The nonlinear residuals, measurement-noise channel, incomplete-measurement formulation, and limitations of the linearized characteristic-root approach are stated explicitly so that the numerical section can assess robustness, sensitivity, and the effective region of attraction of the nonlinear closed loop.

Applied Sciences doi: 10.3390/app16115348

Authors: Zhimeng Chen Kejia Hu Junqiang Liu Yinkai Ji Yi Zhu Hualun Chen Chao Yuan Zhiyu Chen

Underground duct cables are widely used in urban power distribution systems, but their enclosed installation environment makes defect inspection difficult, labor-intensive, and potentially hazardous. Infrared thermography can capture abnormal temperature distributions caused by insulation degradation, conductor damage, sheath failure, or severe structural defects, while robot-based inspection provides a promising solution for confined duct environments. However, thermographic fault detection for underground small-diameter duct cables remains insufficiently studied, and practical deployment requires lightweight models suitable for embedded edge devices. In this study, an improved YOLO26-based thermographic fault detection framework is proposed for underground duct cable inspection. A Cable-Thermo dataset is constructed using an ANSYS 2025 R2-based thermoelectric coupling simulation, covering four defect categories: hollow-type damage, conductor burnout, sheath damage, and severe damage. To balance detection accuracy and deployment efficiency, two model variants are developed. YOLO26-Thermo-E retains the original detection scales and integrates CDA and SimSPPF modules for accuracy-prioritized diagnosis. YOLO26-Thermo-H further removes the small-scale detection branch as a deployment-oriented design choice, based on the scale distribution observed in the simulation dataset, where most fault-induced thermal anomalies appear as spatially continuous medium- or large-scale regions. This design assumption still requires further validation using real duct thermographic data. Experiments show that YOLO26-Thermo-E achieves the highest mAP50 of 99.20%. YOLO26-Thermo-H maintains a mAP50 of 99.00% while reducing GFLOPs by 34.3% and parameters by 16.2% compared with YOLO26. On an NVIDIA Jetson Orin NX, YOLO26-Thermo-H reaches 34 FPS under FP16 inference and 45 FPS under INT8 inference. These results demonstrate the feasibility of the proposed framework under controlled simulation conditions and its potential for edge deployment. The limitations of the simulation-based dataset are also discussed, and future work will focus on real-scene data collection and simulation-to-real generalization.

Electronics doi: 10.3390/electronics15112311

Authors: Sicheng Jin Kai Hu Shuai Shen Yongkai Cai Chengxue Cai

Skeleton-based human action recognition has achieved significant progress, but local occlusions and missing joints in complex environments (e.g., occlusion and low-light conditions) still degrade recognition accuracy and stability. Existing GCN-based methods aggregate features uniformly across joints and lack mechanisms to suppress unreliable observations or recover structural semantics under large-area occlusion. To address this, we propose a Robust Occlusion-Compensated Graph Convolutional Network (ROC-GCN) with two complementary components: an adaptive dropout module that suppresses spatiotemporal noise via attention-guided Bernoulli sampling with dynamic spatial–temporal fusion, and an Occlusion Compensation Graph Convolution Module that compensates occluded features through Local–Global Body-Prior-Guided Attention together with feature-guided and multi-hop aggregation. To enable systematic evaluation, we further construct two complementary occlusion benchmarks on NTU RGB+D 60/120 covering spatial-random and spatiotemporal-continuous occlusion, and additionally validate the model on a real-world missing-joint subset. On standard NTU60/120 X-Sub, ROC-GCN improves Top-1 accuracy by +0.41% and +0.48% over the baseline, with the Top-1 standard deviation reduced from 0.61 → 0.17 and 0.47 → 0.10. On the occlusion benchmarks, Top-1 accuracy further improves by +0.98% and +0.73%, and consistent gains are also observed on the real-world missing-joint validation, confirming improved robustness and training stability.

Encyclopedia doi: 10.3390/encyclopedia6060116

Authors: Vassilios Makrakis

This entry focuses on an Aristotelian approach to contemporary discourses about the implications of Artificial Intelligence (AI) regarding what it teaches and learns, with special regard to virtue or arete, practical wisdom or phronesis, and human flourishing or eudaimonia. Even though AI technologies provide new options for personalized learning, adaptive assessment, and data-driven instruction, their increasing entrenchment in the education ecosystem raises fundamental philosophical questions about the essence of teaching and learning, and about how we become better people. Aristotle’s distinction between intellectual and moral virtues can help us determine whether AI meaningfully contributes to the cultivation of good judgment, ethical character, and responsible agency. While AI is not completely antithetical to virtue formation, its knowledge and skill acquisition cannot replace the social, experiential, and habituated processes through which virtues are grown. AI should be designed and deployed as a “technological partner” to support (not replace) the teacher’s moral and pedagogical role. Guided by Aristotle’s view of eudaimonia and the common good, this analysis suggests that education should be structured to promote human flourishing in the age of AI, ensuring that learners develop their capacities for ethical reasoning, autonomy, and co-responsible participation to build a more sustainable and just society.

Applied Sciences doi: 10.3390/app16115347

Authors: Mieczysław Cichoń Kinga Cichoń

This paper presents a generalized epidemic modeling framework based on g-tempered Caputo fractional derivatives with discrete time delays. The proposed approach incorporates nonlocal memory effects, nonlinear temporal scaling, and delayed epidemiological responses within a unified mathematical structure. The introduction of the nonlinear time transformation g(t) and the tempering parameter λ eliminates the unrealistic infinite-memory behavior associated with classical power-law kernels while simultaneously introducing new challenges related to parameter identifiability and inverse problems. We investigate the structural properties of the resulting dynamical systems and show that the associated inverse problem is inherently ill-posed. To illustrate the practical implications of these results, the framework is applied to a delayed SIQR epidemiological model. Numerical simulations are performed using a generalized L1-type scheme adapted to delayed fractional histories, and a multi-phase parameter estimation procedure is proposed to address the ill-posedness of the reconstruction problem. The results demonstrate the ability of the model to capture both short- and long-term memory effects in epidemic evolution while highlighting the challenges of statistical identifiability in generalized fractional systems.

Applied Sciences doi: 10.3390/app16115346

Authors: Fran Oficial-Casado Alexis Soriano-Gandia Jose Ignacio Priego-Quesada

Background: Cycling performance depends on both aerobic capacity and neuromuscular function, with recent training approaches emphasizing the role of strength training. However, the extent to which neuromuscular characteristics assessed in conventional strength exercises transfer to cycling performance remains unclear. Therefore, the aim of this study was to analyze the relationship between the Load–Velocity (L-V) profile obtained from a multi-joint strength exercise (half squat) and the cycling Power Profile (PP) in Masters-level cyclists. Methods: Twelve masters-level cyclists were evaluated by the L-V and the PP test. The cycling PP was determined through maximal efforts of 1, 5, and 20 min, expressed relative to body mass (W·kg−1). The L-V profile was assessed during the half squat using a progressive loading protocol with load–velocity monitoring. Pearson’s correlation analyses were performed between the slope and intercept of the L-V profile relationship and PP variables, as well as mean ascent velocity (VAM). Results: No significant relationships were observed between L-V profile variables and cycling performance (r = −0.21 to 0.09, p > 0.05). In contrast, VAM showed very large associations with P1 (r = 0.81, p = 0.001) and P5 (r = 0.86, p < 0.001). The regression model explained a large proportion of the variance in VAM (R2 = 0.75, p = 0.01). Conclusions: Strength performance assessed through a conventional exercise such as the half squat is not directly related to cycling PP in masters-level cyclists. The observed relationships between VAM and cycling PP reinforce the importance of task specificity.

Brain Sciences doi: 10.3390/brainsci16060563

Authors: Luca Petrigna Alessandra Amato Claudio Di Brigida Salvatore Spinella Giuseppe Evola Giuseppe Musumeci

Background/Objectives: The number of people who practice gym activities is increasing. Most gym activities take place within a building, and the movements are controlled, making them closed-skill activities. This could decrease the processing speed capacity. The objective was to investigate whether a difference, assessed by a simple reaction time and muscle stiffness task, exists between people who practice gym versus open-skills sports activities. Methods: A total of 58 gym users and open-skills sport practitioners were recruited. Participants’ anthropometric characteristics were evaluated. Electrodes were set at the tibialis anterior (TA) and gastrocnemius lateralis (GL), and participants performed the simple reaction time task. A drop jump test (muscular stiffness) was also executed. A multiple comparison test was adopted to study the differences between groups for FAT%, reaction time, and ground contact time. The significance level was set at p ≤ 0.05. Results: Data from the groups presented no statistically significant differences in the simple reaction time task (p = 0.999) and in the drop jump (p = 0.999), or from a superficial electromyography point of view. Conclusions: This exploratory study detected no statistically significant differences between the groups. The study design does not support equivalence conclusions. Further studies are required to understand the topic in depth.

Symmetry doi: 10.3390/sym18060909

Authors: Wanqin Jiang

Fatigue driving causes 20–30% of global traffic accidents. To address limitations in feature fusion and real-time performance, this study proposes an improved You Only Look Once version 8 (YOLOv8)-Long Short-Term Memory (LSTM) model with symmetry-aware spatiotemporal feature learning. In the spatial phase, Group Shuffle Convolution (GSConv) and Slim Neck structures are introduced to enhance facial feature detection while reducing parameters by 32.3%. In the temporal phase, an improved Inverted Transformer(iTransformer) with differential attention is integrated with an LSTM-Feed-Forward Network (FFN) architecture, achieving a 90.1% prediction accuracy and an 84.6% noise suppression rate. A standardized dataset of 13,200 images was constructed using a four-level classification system. By implementing TensorRT acceleration and multi-process parallel frameworks, the system optimizes single-frame latency to 38 ms—a 9.5× efficiency gain—while maintaining an overall detection accuracy of 92.4%. These results demonstrate that the proposed framework effectively balances model lightweighting with high precision, providing a robust and efficient solution for real-time driver monitoring in complex driving scenarios.

Electronics doi: 10.3390/electronics15112310

Authors: Jungwoo Lee Hyogon Kim Sung-Jo Yun Youngho Choi

The deployment of object detection models on resource-constrained edge devices remains a substantial challenge, primarily because conventional static networks expend the same worst-case computational cost on every input, regardless of intrinsic difficulty. This paper proposes an input-adaptive dynamic neural network architecture for object detection in embedded environments. The present study investigates two orthogonal axes of input-adaptive inference for embedded object detection: The system demonstrates depth adaptivity through the implementation of Early Exit, and width adaptivity via group-wise Adaptive Routing. The proposed framework is constructed on a frozen Ultralytics YOLO26s backbone and incorporates two YOLO-style early-exit heads positioned at approximately 33% and 66% of the backbone depth. Furthermore, the framework incorporates two Straight-Through Gumbel-Softmax routers, which are appended after Layers 4 and 8 with group-wise hard gating. Both axes additionally accept an explicit external control signal that allows the host system to override the input-conditional policy at inference time. The dual-mode design facilitates the functionality of the trained checkpoint as either an input-adaptive policy, in which the depth and width are determined per sample from the input distribution, or an externally controlled policy. The experimental findings demonstrate two strongly asymmetric input-adaptive policies on a frozen YOLO26s backbone. The early-exit profile reduces the compute per sample from 12.739 to 10.532 GFLOPs—a 17.32% reduction according to our in-house Conv2d/Linear MAC-based GFLOPs estimator—while preserving baseline accuracy (mAP50 = 0.1545 vs. baseline = 0.1528; ΔmAP50 = +0.0017, within evaluation noise; mAP50–95 = −0.0033). Evaluating the router-only profile in the same validator pipeline with a sparsity penalty of γ = 0.05 results in a 12.3% decrease in logical GFLOPs (from 12.739 to 11.172), while maintaining an accuracy level that is at or above the PEFT baseline (mAP50 = 0.2324 and mAP50–95 = 0.1040). In our small-domain PEFT setup, training the dynamic-policy modules yields per-checkpoint mAP shifts in this magnitude. Therefore, we interpret the width-axis accuracy result as preservation of the baseline rather than an improvement. Our contribution on the width axis is reducing computing power while maintaining baseline accuracy. Importantly, the router profile’s logical GFLOP savings are not currently reflected in wall-clock latency under our dense-kernel PyTorch implementation. Achieving practical speedup requires sparse-kernel deployment, such as structured-sparse kernels, TensorRT, TVM, or Triton paths. We will address this in future deployment-level work. Our results indicate that the depth axis can yield genuine end-to-end speedup today, while the width axis offers deployment-pending compute reduction.

Applied System Innovation doi: 10.3390/asi9060108

Authors: Luong Duc Long Vo Thi Dinh Dinh Khanh Nguyen Quang Trung Truong Ngoc Son

This study proposes an intelligent decision-support framework for alternative selection in complex construction projects, where evaluation processes are affected by uncertainty, multiple decision-makers, and interdependent criteria. The framework integrates the fuzzy group best–worst method with fuzzy TOPSIS into a unified structure that explicitly captures cross-criterion influence effects. First, triangular fuzzy judgments from multiple experts are used to derive criterion weights, while interdependencies among criteria are represented through a fuzzy influence-intensity matrix and incorporated into fuzzy nonlinear optimization models. This process enables the systematic estimation of both independent and interdependency-adjusted criterion weights. Second, the resulting weights are used in a fuzzy ranking procedure to evaluate alternatives according to their relative closeness to fuzzy ideal solutions. To enhance transparency, reproducibility, and practical usability, the proposed method is implemented in Python as an automated computational workflow for decision analysis. Its applicability is demonstrated through a real-world case study on access platform system selection for mechanical, electrical, and plumbing installation in an airport terminal subject to safety, productivity, workspace, and elevation-related constraints. The results show that explicitly modeling criterion interdependencies provides a more realistic evaluation structure and enhances the robustness and reliability of alternative selection in complex construction management contexts.

Axioms doi: 10.3390/axioms15060401

Authors: Saddaf Noreen Muhammad Imran Muhey U. Din Zhang Wei Adil Murtaza

The article derives sufficient conditions under which the normalized Rabotnov function becomes q-close-to-convex relative to specific starlike functions on the open unit disk. To enhance the impact of our results, we include some consequences derived from the main theorems, along with graphical illustrations. The starlikeness of the Rabotnov function with respect to different aspects also falls within the scope of this study.

Fractal and Fractional doi: 10.3390/fractalfract10060360

Authors: Wenxiong Chen Yahong Guo

The averaging effect Chen: All italics are used to draw readers’ attention, and if they do not conform with the Journal style, you can remove them. is a distinctive property possessed by fractional operators. In recent years, it has emerged as a powerful tool in the study of qualitative properties of solutions to fractional elliptic and parabolic equations. In this article, we systematically summarize and prove various forms of the averaging effects for both fractional elliptic and parabolic equations, from the simplest one to the one under very relaxed conditions, including versions for antisymmetric functions. We then present examples to illustrate how to apply these effects to obtain radial symmetry and monotonicity for solutions in a unit ball and in a half space. In addition, we derive averaging effects for fractional Monge–Ampère operators and for fractional p-Laplacians, which will be potentially applied to obtain qualitative properties for solutions to equations involving these operators. Compared with the traditional approaches, methods based on the averaging effect require substantially weaker regularity assumptions and can even accommodate unbounded solutions.

Systems doi: 10.3390/systems14060611

Authors: Camelia Oprean-Stan Diana Elena Vasiu Renate Doina Bratu Sebastian-Emanuel Stan

Against the backdrop of the expansion of sustainable finance and the growing relevance of ESG-related information, disclosure and regulation, this paper examines the dynamic relationship between sustainability-related uncertainty and ESG equity market volatility in a global framework. Sustainability-related uncertainty is proxied by the Global GDP-Weighted ESG-Based Sustainability Uncertainty Index (ESGUI), while ESG market volatility is measured through a monthly proxy constructed from estimated daily conditional variances obtained from GJR-GARCH(1,1) models with Student-t innovations. The paper explicitly distinguishes sustainability-related uncertainty, understood as ambiguity in the ESG information environment, from ESG market volatility, understood as market-pricing instability in ESG equity benchmarks. Empirically, the study combines bootstrap full-sample Granger-causality tests, parameter-stability diagnostics, and rolling-window bootstrap analysis. Robustness and extended analyses use an EGARCH-based volatility proxy, alternative rolling-window lengths, macro-financial controls, an emerging-market ESG benchmark, impulse-response analysis, forecast-error variance decomposition, and out-of-sample forecasting tests. The full-sample results indicate an asymmetric predictive pattern: ESG market volatility contains Granger-causal predictive information for changes in sustainability-related uncertainty, whereas the reverse direction is not supported on average. However, parameter-stability tests reject constancy, and rolling-window evidence shows that predictive effects arise episodically in both directions, with changes in sign, magnitude and significance. The uncertainty-to-volatility channel becomes statistically relevant and locally stronger during stress episodes, especially around 2019–2021, while macro-control results show that broader market stress absorbs part of the volatility-to-uncertainty linkage. The findings indicate a regime-dependent uncertainty–volatility nexus and support dynamic approaches to ESG risk monitoring, portfolio management and regulatory communication. All results are interpreted as predictive evidence, not structural causality.

Electronics doi: 10.3390/electronics15112309

Authors: Zhuofeng He Sunpei Shang Yumeng Guo Aojie Zhou

REST APIs have become a dominant interface for modern web applications and cloud services, and a growing body of work has studied automated testing and reproducible error discovery for such systems. Prior approaches have explored dependency inference, cross-request value reuse, and, more recently, learning- or LLM-based test generation. However, deep stateful multi-step reproducible error discovery remains difficult in practice because sequence construction is still often performed directly in the endpoint space, reusable runtime artifacts are not always tightly coupled with sequence expansion, and online LLM-driven generation may introduce cost and instability. We present Spec2SeqFuzz, a stateful multi-step fuzzing framework for REST API systems. The central idea is to guide online exploration in a compact category space rather than directly in the full endpoint space. Spec2SeqFuzz uses LLMs only in an offline pre-processing stage to normalize public multi-step PoCs, classify OpenAPI endpoints into a transferable category taxonomy, and construct training data for next-category prediction. During online fuzzing, the framework predicts the next likely API category from the executed prefix and observed response feedback, maps the predicted categories back to concrete endpoints, and combines this guidance with black-box endpoint fuzzing, proxy-based payload collection, and snapshot-assisted state restoration. We implemented a prototype and evaluated it on GitLab and WordPress, using MINER as the primary reproduced baseline in our current study. The results show that Spec2SeqFuzz is promising for both multi-step and single-endpoint error discovery on these two targets. Following the terminology used in MINER, we report reproducible errors rather than treating every triggered failure as a confirmed security vulnerability. Across the two targets, Spec2SeqFuzz discovers more reproducible multi-step errors than MINER, while the ablation results further suggest that category guidance, payload reuse, and depth-first stateful exploration are important to the final error-discovery performance.

Buildings doi: 10.3390/buildings16112128

Authors: Carlos Manuel González-Gutiérrez Luciano Roberto Fernández-Sola Manuel Eurípides Ruiz-Sandoval

This study evaluates the impact of Dynamic Soil–Structure Interaction (DSSI) on the efficiency of an algorithm based on the existing literature on Vibration-Based Structural Health Monitoring (VBSHM). The algorithm is designed for Level 3 detection, that is, to accurately estimate the presence, location in height, and extent of structural damage simultaneously. Using computer simulations of a hypothetical two-dimensional six-story symmetrical reinforced concrete building, the study analyzes the algorithm’s performance under increasing soil flexibility. Efficiency is measured through four key metrics: the number of false positives and negatives, a weighted stress index, the iterations required for damage intensity estimation, and the accuracy of the identified versus simulated stiffness reduction. Results indicate that the algorithm remains effective even when input motions correspond to actual soft-soil ambient vibration recordings modified by kinematic DSSI effects, despite frequency contents differing from white-noise conditions. Conversely, inertial DSSI negatively impacts performance, leading the VBSHM algorithm to underestimate damage as soil deposits become softer.

Buildings doi: 10.3390/buildings16112133

Authors: Pengchen Chen Chuang Wang Jingjing An

Accurate and stable indoor occupancy information is essential for occupant-based intelligent ventilation control. Under a single-camera setting, existing indoor occupancy detection methods commonly suffer from missed detections caused by occlusion and blind zones, false detections caused by people outside the room, and cumulative entry–exit errors that are difficult to correct. These problems lead to false fluctuations in detected occupancy, affect control performance, and may further reduce indoor comfort or cause unnecessary energy use. To address the practical situation in which indoor spaces are commonly equipped with a single security camera, this study proposes an indoor occupancy detection method by fusing field-of-view information and entry–exit events with a single camera. The study covers method development, multi-scenario validation, parameter analysis, and a ventilation control application. The proposed method uses YOLOv8x and DeepSORT as front-end models and performs post-processing on their outputs to extract field-of-view occupancy information, entry–exit events, and blind-zone events. An occupancy confirmation and correction module is then constructed. The blind-zone event mechanism reduces the influence of missed entry–exit events and camera blind zones on occupancy judgment. The correction module integrates frame-by-frame ID counts, historical outputs, and multiple event signals to verify and suppress false occupancy changes caused by false detections, missed detections, and blind zones, thereby producing more stable indoor occupancy results. Experimental results show that the proposed method outperforms the baseline methods based on front-end object detection and tracking in terms of score, RMSE, and F1 score in three typical scenarios: an office, a home, and a classroom. In the office scenario, the proposed method achieved a score of 99.36%, an RMSE of 0.081, and an F1 score of 0.781. The detection stability was also improved in the home and classroom scenarios. In the high-density and strongly occluded classroom scenario, the absolute detection performance of the fusion-based detection method was limited by the front-end models, indicating that the method still has certain applicability boundaries in complex high-density scenes. Parameter sensitivity analysis shows that key parameters, including the entry–exit area depth, confidence threshold, and time threshold, affect the detection results of the fusion-based detection method. Under the test conditions of this study, the method performs well when the entry–exit area depth is approximately 1.5d, the YOLOv8x confidence threshold is 40%, and the time threshold is 5 × FPS. These results can provide a reference for initial parameter setting and on-site calibration in similar scenarios. Using the office scenario as a case study, the method was further applied to occupant-based ventilation control. The average CO2 concentration during occupied periods under the proposed method was 622.43 ppm, which was closest to the result under ground-truth occupancy control, with a deviation of only 0.9 ppm. This indicates that the method can help improve indoor air quality. Compared with conventional schedule-based control, occupant-based ventilation control driven by the proposed fusion method reduced cumulative fan energy consumption by approximately 65.2%, showing good energy-saving potential at the ventilation-control level. In summary, the proposed method can effectively improve the accuracy and stability of indoor occupancy detection under a single-camera setting and provide more reliable input for occupant-based ventilation control. The framework is modular, and the front-end object detection and tracking models can be replaced according to actual deployment needs. However, the validation in this study is still mainly based on scenarios where existing security cameras can cover the main activity areas and all entry–exit passages. The applicability of the method under more complex camera arrangements, lighting variations, and automatic region configuration requires further investigation.

Applied Sciences doi: 10.3390/app16115344

Authors: Ilare Bordeasu Dorin Bordeasu Brandusa Ghiban Daniel-Catalin Stroita Liviu-Daniel Pirvulescu Imre Kiss

The degradation of material surface structures caused by cavitation is of a mechanical nature due to cyclic local fatigue loading. Its intensity depends on both the material’s mechanical properties and its surface microstructure. Evaluating the surface structure resistance to cavitation loading can be performed based on experimentally determined mechanical properties and/or through macro- or microscopic analysis of the eroded structure. Manufacturers, designers, and users of hydromechanical equipment operating under cavitation conditions are interested in materials whose properties and structures can withstand cavitation loading. For this reason, the current experimental research in the field focuses on establishing relationships that express the influence of either mechanical properties or surface microstructure on the resistance of material structures to cavitation erosion. The current paper aligns with this research direction and aims to determine statistical relationships between mechanical properties and the cavitation erosion resistance of aluminum-based alloys. The mechanical properties considered include ultimate tensile strength (Rm), yield strength (Rp0.2), surface hardness (HB), resilience (KCU), and elongation at fracture (A5). Cavitation resistance is evaluated using the parameter Rcav, defined according to the ASTM G32-2016 standard. The experimental results were obtained from cavitation tests conducted using a standard vibratory device that complies with ASTM G32 requirements.

Applied System Innovation doi: 10.3390/asi9060107

Authors: Jonathan Matthei Johannes Maas Maurice Wischum Sven Mackenbach Katharina Klemt-Albert

Public participation is a central component of democratic decision-making processes, particularly in planning and approval procedures. However, increasing data complexity and the growing number of submitted objections significantly raise the effort required for their review and processing. Against this background, this study developed an AI-supported objection management system that uses a large language model (LLM) to automatically pre-sort objections by topic and generate response suggestions based on historical objection texts from previous infrastructure projects. The aim is to increase efficiency in the processing workflow while maintaining consistent response quality without replacing human decision-making. The prototype development is preceded by a literature review to identify key user requirements and derive relevant use cases. Subsequently, four expert workshops with representatives from German road and rail infrastructure administrations at the state and federal level were conducted to evaluate the prototype. The results indicate significant efficiency potential, particularly through automated thematic pre-sorting of objections. However, topic structures must be adapted to the specific procedure. AI currently mainly serves as supportive pre-processing and requires human review (“human-in-the-loop”). Transparent labeling of AI use is also necessary to ensure traceability and acceptance. The findings will be incorporated into the ongoing development of the prototype within the BIM4People research project funded by the German Federal Ministry of Transport (BMV), with the aim of further improving the system’s functionality and exploring additional applications.

Mathematics doi: 10.3390/math14111849

Authors: Umar Hasan Muhammad Ali Nayeem

Large Language Models (LLMs) are typically safety-aligned using high-resource language data, but it remains unclear whether these constraints transfer reliably across distinct linguistic manifolds. This study examines the mathematical foundations of cross-lingual guardrail degradation using Bengali as a low-resource test case. We evaluate Meta-Llama-3-8B, Gemma-2-9B, and Llama-Guard-3 through an automated English-to-Bengali translation pipeline, paired statistical testing, latent-space visualization, and tokenization-based structural analysis. The results show a statistically significant increase in Gemma-2’s Attack Success Rate from 32.0% in English to 41.2% in Bengali (p<0.0001, McNemar’s test), while Llama-Guard-3 fails to detect 39.5% of malicious Bengali prompts. Latent-space projections indicate weaker separation between safe and unsafe Bengali representations, and tokenization analysis shows a 4.69-fold token fertility expansion associated with a normalized perplexity of 887.32. Furthermore, projecting low-resource inputs back into the high-resource latent space successfully restores optimization constraints, whereas natively translating safety prompts exacerbates vulnerability. Together, these findings suggest that cross-lingual safety failures are associated with representational entanglement and token fragmentation rather than only superficial prompt translation effects. The study supports the need for multilingual alignment methods that better account for tokenization geometry, latent-space structure, and language-dependent safety evaluation.

Entropy doi: 10.3390/e28060593

Authors: Tao Guo Zhangyao Song Huihui Wu Yang Li

This paper analyzes the semantic rate–distortion problem motivated by task-oriented data compression with side information. The semantic information related to a task is not directly accessible to the encoder but implicitly impacts the observations through a joint probability distribution. The decoder aims to simultaneously recover the observation and infer the semantic information under certain distortion constraints. Notably, this paper advances the related research by involving side information and the observation of two semantic segments at both the encoder and decoder, which significantly complicates the theoretic analysis. We establish the information-theoretic limits for the tradeoff between compression rates and distortions by fully characterizing the rate–distortion function. Additionally, we explicitly derive the corresponding rate–distortion functions under specific Markov conditions for two scenarios: (i) the task is a binary classification of an integer observation as even and odd; and (ii) Gaussian-correlated task and observation. Furthermore, we validate the information-theoretic analysis by conducting a classification-oriented lossy image compression based on deep learning. The results are consistent with theoretical expectations, demonstrating the effectiveness of side information on both distortion and classification accuracy and the rationality of semantic segmentation.

Applied Sciences doi: 10.3390/app16115343

Authors: Zhiqi Yang Hao Zhang Zhigang Xu Shihong Ge

This paper addresses an integrated production–transportation scheduling problem in assembly workshops, encompassing the processes of part machining, material handling via handling resources, and final synchronous assembly. The finite buffer capacities of production resources can cause blocking, thereby reducing efficiency. Material handling resources are subject to different service area restrictions, and some share safety zones with production resources, preventing simultaneous processing. To address this, a mixed-integer programming model is formulated with makespan and total empty travel time as bi-objective optimization targets. Since the mixed-integer linear programming (MILP) model faces difficulties in solving medium- and large-scale instances, an improved memetic NSGA-II algorithm (IMNSGA-II) is proposed. The algorithm adopts a three-segment chromosome encoding and incorporates a VNS-SA local search mechanism within the global evolutionary framework of NSGA-II. Small-scale computational experiments using Gurobi are first used to verify the correctness of the model. Decoupling experiments further demonstrate the necessity of integrated optimization: compared with phased baseline methods, IMNSGA-II reduces makespan and empty travel time by approximately 10.16% and 12.33%, respectively. In ablation and comparative experiments, results based on hypervolume (HV) and inverted generational distance (IGD) show that the proposed method achieves better convergence, diversity, and overall Pareto front quality than multiple baseline algorithms. These experiments confirm the effectiveness of the proposed model and algorithm.

Infrastructures doi: 10.3390/infrastructures11060185

Authors: Xing-Ze Duan Zhao-Jun Liu Shuai-Qi Wang Rui-Jie Xia Wei Li Ju Liu Guo-Hua Song Zhi-Xiao Shi Jun Shi Ao Yang Kuang-Yu Dai

To balance ultra-early strength development and workable time in cement-based grouting materials for rapid repair applications, an ultra-early high-strength grout system was developed by regulating the dosage of an accelerating agent (CF), retarder content, and water-to-binder ratio (w/b). The effects of these parameters on setting behavior, workability, mechanical properties, volumetric stability, and durability were systematically investigated. X-ray diffraction (XRD) and scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM/EDS) were further conducted to qualitatively evaluate the hydration characteristics and microstructural evolution of the optimized system. The results showed that CF accelerated early hydration and promoted the rapid formation of ettringite (AFt), which contributed to the development of ultra-early strength. The incorporation of a retarder effectively prolonged the workable time and improved slurry workability. Increasing the w/b ratio enhanced flowability and toughness, although excessive w/b reduced compressive strength. The optimal mixture contained 30% CF, 0.02% retarder, and a w/b ratio of 0.19. Under this condition, the grout exhibited a flowability of 312 mm and compressive strengths of 81.4 MPa at 1 h and 121.3 MPa at 28 d. In addition, low air shrinkage (0.027% at 28 d) and excellent chloride penetration resistance (12 C at 28 d) were achieved. Microstructural observations suggested that the dense structure formed by AFt and C–S–H gel contributed to the improved macroscopic performance. This study provides an engineering-oriented reference for the mix design and performance optimization of ultra-early high-strength cement-based grouting materials for rapid repair applications.

Processes doi: 10.3390/pr14111734

Authors: Yufeng Shen Yu Song Jian Yi Wentong He Xuanlong Shan Ang Li Ying Bian Nan Jiang Shuyang Wang Yongbo Zhang

Oil shale in situ conversion provides an important pathway for developing medium- to deep-buried, low-grade, and thin oil shale resources. Among the available approaches, the in situ conversion technology triggered by the topochemical reaction method, hereafter referred to as the TSA method, induces local oxidation reactions of pyrolysis residuals, fixed carbon, and reactive organic matter through preheating and oxygen-containing gas injection. The released in-formation heat then supports continued kerogen cracking and reaction-front propagation. This review summarizes the TSA method from a process-oriented perspective, linking reaction mechanisms, engineering controls, geochemical process identification, pilot tests, economic–environmental constraints, and scale-up evaluation. Existing studies indicate that the TSA method has formed a technical chain involving reaction initiation, heat/reaction-front propagation, oil and gas recovery, and process monitoring. Pilot tests provide evidence for operational feasibility, but not yet for full commercial feasibility. Thermal simulation results show that oil and gas generation and expulsion become significant above ~350 °C, and that 375–425 °C can be used as an important reference window for temperature control rather than a fixed optimum for all oil shale reservoirs. Geochemical indicators can provide complementary constraints for identifying reaction progress, especially when calibrated with produced oil and gas. Further development should focus on fracture-network control, heat-transfer enhancement, oxygen-supply regulation, multi-well coordination, equipment reliability, economic evaluation, groundwater protection, and CO2 emission accounting. These issues are critical for advancing the TSA method toward larger-scale, low-carbon, and well-regulated application.

Entropy doi: 10.3390/e28060592

Authors: Jiangnan Zhou Sheng Zhang Bing Wu Qiuming Wang Chennan Wu Ziqiang Luo Ka Sun Hongmei Mao

Existing methods for hypergraph node classification usually rely on local message passing and use a unified strategy for topological modeling across hyperedges of different sizes. However, they have two limitations in semi-supervised settings. First, representation learning mainly depends on local neighborhoods, making it difficult to incorporate global topological information. Second, a unified structural modeling strategy cannot effectively handle both small and large hyperedges. Small hyperedges require modeling fine-grained local relations, while large hyperedges need sparse group-level structure. To address these issues, we propose S2-HGNN, a scale-aware hypergraph node classification framework with spectral inductive bias for semi-supervised learning. S2-HGNN first injects global topological information into the input features using complementary hypergraph spectral operators. It then constructs different auxiliary topologies based on hyperedge size. For small hyperedges, it uses Top-k constrained clique expansion to preserve representative local relations. For large hyperedges, it uses star expansion to reduce redundant connections while preserving sparse group-level structure. Finally, node representations are jointly learned from the original hypergraph backbone and the two auxiliary branches, and final predictions are obtained through node-level adaptive fusion. Experiments on multiple public datasets show that the proposed method consistently outperforms strong baselines and exhibits superior robustness under feature perturbations.

Mathematics doi: 10.3390/math14111848

Authors: Alaa H. El-Qadeem Shams Alyusof Rabab Alyusof Mohamed A. Mamon

The current investigation aims to explore the classical open problem concerning the Ma–Minda starlike family, employing the subordination principle to generate a novel family of bi-univalent analytic functions. Upon using the Faber polynomial technique, we derive the coefficient estimates and the optimal upper bound of Hankel determinant. Furthermore, we discuss earlier studies relevant to our results.

Mathematics doi: 10.3390/math14111847

Authors: Dipesh Jagjit Singh Dhatterwal Hacer Ozden Ayna

This paper presents a reinforcement learning (RL) framework based on the Koopman operator for high-dimensional nonlinear control. By leveraging nonlinear eigenvalue dynamics, the approach enables scalable and efficient policy optimization. We examined the challenge of controlling complex systems by embedding high-dimensional states xt∈Rn into a Koopman-invariant subspace ϕx∈Rm, where evolution becomes linear under the Koopman operator K. By spectrally decomposing K=UΛU−1, the eigenvalue dynamics are obtained, and K is reconstructed iteratively via dominant eigenpairs vi,wi. A policy network πa|s selects actions ut, while a value function Vs, expressed in Koopman eigenfunction coordinates, guides gradient-based policy updates. The framework integrates spectral stability constraints (ρX<1) and Lyapunov-based analysis to ensure convergence. We derive perturbation bounds for Koopman eigenvalues under policy updates and establish conditions for nonlinear mode interactions in the lifted space. The spectral policy gradient theorem for Koopman RL links eigenvalue dynamics to policy optimization, includes a constrained Bellman formulation in Koopman coordinates, and analyzes bifurcation of learning-induced eigenvalue shifts.

Buildings doi: 10.3390/buildings16112132

Authors: Xinzheng Shi Rongyue Zheng Chenzhen Ye

To investigate the dynamic response and damage mechanisms of reinforced concrete (RC) shear walls subjected to the combined action of contact explosions and axial compression, three contact explosion tests were conducted on RC shear walls with a constant axial compression ratio of 0.15 and different charge masses. A self-balancing axial loading device was designed to maintain a continuous and stable axial force during the blast tests. Finite element models were subsequently established using LS-DYNA. The fluid structure interaction (FSI) method was adopted, and the numerical models were validated in terms of crater dimensions and failure modes. Based on the validated model, a numerical parametric study was further conducted to examine the influence of axial compression ratios ranging from 0 to 0.4. The results indicate that the axial compression ratio has a non-monotonic effect on the local damage dimensions of RC shear walls. The wall exhibited a relatively smaller crater area and a more favourable local damage response when the axial compression ratio was approximately 0.2. As the charge mass increased, the failure mode progressively changed from cratering and spalling to perforation and punching shear failure. Based on the experimental and numerical results, empirical relationships were proposed to correlate the crater diameters on the front and rear faces with the charge mass and axial compression ratio. These relationships enable the rapid estimation of local damage dimensions using a limited number of input parameters.

Processes doi: 10.3390/pr14111732

Authors: Congshi Li Zhengxun Lv Shouguo Song Ke Bian Jingxi Zhang Lei Kou

To improve the accuracy of cutter wear and service life prediction for disc cutters, an improved normal force model is established based on the traditional CSM model by considering the supporting force and friction acting on the disc cutter from the side crushing zones. By incorporating the micro-mechanism of abrasive wear, an analytical model for the radial wear of the disc cutter and a service life prediction model are derived. Meanwhile, a regression model for cutter wear is established based on field operational parameters and cutter wear data. The mechanistic model is validated using field data from a tunnel project in Guangdong, China, and the results show that the average prediction errors of wear and service life are 8.13% and 8.85%, respectively, which are significantly lower than those of the traditional CSM model. Further comparative analysis between the two types of models is conducted, and the results indicate that the regression model achieves average prediction errors of 7.57% and 7.86% for wear and service life, respectively, showing higher prediction accuracy than the mechanistic model. The results demonstrate that the mechanistic model is suitable for revealing the wear mechanism of the disc cutter, while the regression model is more applicable for engineering prediction, and the two approaches can be used in a complementary manner.

Information doi: 10.3390/info17060527

Authors: Yabin Peng Chenyu Zhou Yuchen Liu Kunlin Li Fan Zhang Shaoxun Liu

Graph neural networks (GNNs) are highly effective on relational data, yet their performance degrades sharply when graph topology is poisoned before training. Existing defenses usually assume a fixed attack pattern and a fixed graph structure, which makes them brittle when the poisoned graph changes across attacks, perturbation budgets, or deployment conditions. We propose GADD, a game-inspired adversarial distillation framework for robust graph defense. GADD first constructs multiple positive and negative graph views through a homophily-aware graph sampling scheme, allowing the model to learn from both purified and high-risk subgraphs. It then trains a heterogeneous group of student GNNs online, where each student receives global class-distribution knowledge from its peers and local structural knowledge through an adversarial cyclic distillation objective. Finally, GADD replaces uniform ensembling with an entropy-regularized adaptive aggregation rule that assigns graph-adaptive weights according to confidence and inter-model agreement. On Cora, CiteSeer, and PubMed, GADD consistently improves robustness against both Meta and Nettack attacks while preserving clean accuracy. Under the strongest Meta and Nettack settings in the main benchmark, GADD improves the best competing baseline by up to 2.99 and 3.42 percentage points, respectively. Additional ablations show that graph sampling, adversarial distillation, and adaptive aggregation all contribute materially to the final robustness gains.

Buildings doi: 10.3390/buildings16112131

Authors: Yijun Yan Jianhui Feng Guobin Li Jiang He Teng Ma Lina Feng Minjun Wu Bingbing Zhang Zhiguang Zhou

To ensure a reliable electrical isolation point in power systems, the seismic performance assessment of disconnect switches is of critical importance for maintaining operational continuity under earthquake excitations. In this study, a hybrid method combining a convolutional neural network (CNN) and a long short-term memory (LSTM) network is proposed for the seismic intelligent classification and response prediction of disconnect switches. Unlike conventional approaches that rely on finite element simulations or shake table tests with high computational costs, the proposed method learns directly from raw ground motion records. The CNN component is designed to capture local frequency characteristics of input ground motions, enabling automatic classification into low-, medium-, or high-frequency categories. Subsequently, category-specific LSTM models are established to map the ground motion time series to multi-dimensional performance indicators of the disconnect switch. These indicators include top absolute accelerations, bottom shear forces, and relative deformations of porcelain posts. A training set comprising 102 ground motion records is constructed based on numerical simulations of a validated simplified model, while another testing set comparing 21 ground motion records are employed to validate the performance of predicted models. Training and validation results demonstrate that the CNN achieves a great classification accuracy. The LSTM predictions show good agreement with the computed time-history responses, with errors of root-mean-square responses generally within 10%. The proposed method provides a rapid, data-driven alternative to traditional seismic analysis, significantly reducing computational time while preserving prediction fidelity. It also enables the parallel prediction of multiple coupled performance indicators, which is not readily achievable by existing single-output surrogate models.

Buildings doi: 10.3390/buildings16112130

Authors: Anwar Saleem Ergang Xiong Mabor Achol Samuel Mahmood Haris

The addition of sulfoaluminate cement (SAC) to ultra-high-performance concrete (UHPC) enables sustainable high-speed construction due to the high 7-day strength without thermal curing. The fast hydration of SAC, however, endangers the admixture efficacy, which may compromise the structural integrity of the infrastructure components. This study investigates the effect of the physical form of polycarboxylate ether (PCE) superplasticizers on the performance of UHPC with the incorporation of SAC in ambient conditions. A paired experimental design of 32 mixtures compared liquid superplasticizers (LSPs) and powder superplasticizers (PSPs) in various binder compositions (OPC/SAC of 1/4–4/1) and water-to-binder ratios (0.18–0.21) at a constant dosage of admixtures of 1% except where w/b 0.18 (1.5% superplasticizers and 1% retarders were used). Findings indicate that LSPs enhance workability and compressive strength by 45% and 10.03%, respectively. The underlying mechanism is explained by comprehensive microstructural characterization through the use of Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD) and Fourier Transform Infrared (FTIR) spectroscopy. SEM study showed a 23% decrease in porosity, and XRD patterns showed the increased formation of amorphous C-S-H gel for LSPs. The higher levels of Al3+ incorporated into the gel structure (C-A-S-H) of the liquid forms was also verified by FTIR spectra. Mechanically, the research reveals one of the kinetic mismatches, where the rate of SAC hydration is greater than the rate of powder dissolution, which leads to a failure to fully disperse and shear-controlled failures. LSPs, in contrast, make it possible to disperse particles immediately, so the matrices become more dense and shift to axial failure. These results provide practical guidelines to infrastructure engineers to use liquid superplasticizer in SAC-based systems in order to achieve sustainability and reliability in terms of performance in precast and fast-track construction projects.

Processes doi: 10.3390/pr14111733

Authors: Rui Wang He Liu

The complex fracture networks, multiphase flow behavior, and nonlinear flow mechanisms induced by hydraulic fracturing in horizontal wells of shale oil reservoirs pose significant challenges to production evaluation. In this study, a semi-empirical productivity evaluation method for multiphase shale oil systems is developed by integrating production dynamics with flow mechanisms. Three-phase productivity equations for oil, gas, and water are established, explicitly incorporating the underlying flow mechanisms. A nonlinear flow index is introduced to characterize both the stress sensitivity of fractures and the threshold pressure gradient in the matrix. Key unknown parameters, including oil saturation, water cut, stimulated reservoir volume, and nonlinear coefficients, are determined through history matching of production data. The impacts of geological properties, fracturing parameters, operating conditions, and nonlinear flow parameters on oil–gas productivity are systematically investigated using the proposed multiphase semi-empirical model. The model is validated against production data from fractured horizontal wells in a field case, demonstrating its accuracy and applicability. Furthermore, the model enables reliable production forecasting based on the derived productivity relationships. The proposed approach provides a practical and efficient tool for rapid post-fracturing productivity evaluation in shale oil reservoirs.

Fractal and Fractional doi: 10.3390/fractalfract10060359

Authors: Waleed Mohamed Abd-Elhameed Hassan M. Alshehri Mohammed H. Alharbi Omar Mazen Alqubori Amr Kamel Amin Ahmed Gamal Atta

This paper introduces two numerical algorithms for solving the fractional and classical Rosenau–Hyman equations. Certain shifted Pell–Lucas polynomials (SPLPs) are employed. New formulas for these polynomials are developed and used to analyze our proposed algorithms, together with the application of the typical collocation method. The established operational matrices for both integer and fractional derivatives are used to convert the problems, along with their governing conditions, into matrix systems that can be treated effectively. The convergence of the shifted Pell–Lucas expansion is thoroughly investigated by developing new estimates. The high accuracy and efficiency of the proposed numerical algorithms are tested through numerical experiments supported by comparisons with some other techniques in the literature.

Future Internet doi: 10.3390/fi18060285

Authors: Fangfang Shan Lulu Fan Yuhang Liu Zhuo Chen Yifan Mao

Federated Learning (FL) enables privacy-preserving model training in edge and IoT environments. However, in adversarial settings, FL suffers from two key challenges: robustness degradation due to data heterogeneity and poisoning attacks, and runtime instability on resource-constrained devices. Existing work mainly focuses on robustness while overlooking system-level stability. To address this, we propose FedCASKD, a robustness- and stability-aware FL framework. It employs a score-based soft aggregation mechanism to suppress unreliable client updates without requiring a trusted dataset, and introduces a selection-aware bidirectional knowledge distillation protocol to mitigate model drift under Non-IID data. The novelty lies in integrating aggregation and distillation into a unified feedback framework that enhances robustness and stability. Experiments on AGNews and SogouNews show that FedCASKD outperforms baselines under label-flipping attacks and heterogeneous settings. Memory and Out-of-Memory (OOM) tests further demonstrate its superior runtime stability in edge environments.

Buildings doi: 10.3390/buildings16112129

Authors: Han Wang Boya Fan Fangyu Zhu Renqiang Han Hao Yu Jisheng Nie Shaodan Huang

People spend the majority of their time indoors; however, most previous studies on the health effects of particulate matter (PM) and ozone (O3) have used outdoor concentrations as a proxy for personal exposure, which may introduce misclassification bias. Since indoor PM and O3 originate primarily from outdoors, estimating their indoor infiltration levels provides a closer approximation of true personal exposure. This study used data on approximately four million deaths occurring over an eight-year period in Jiangsu Province, China. The infiltration factor method and time-series analysis were employed to assess the linear and nonlinear associations of short-term indoor exposure to outdoor-origin PM1, PM2.5, PM10, and O3 with all-cause, cardiovascular, and respiratory mortality. In addition, the interactions between indoor PM and O3 were investigated. The results indicate that indoor exposure to outdoor-origin PM and O3 was positively associated with mortality, and these associations were stronger than those observed for direct outdoor exposure. Each 10 μg/m3 increase in the 2-day moving average concentration of indoor PM1, PM2.5, PM10, and O3 was associated with a 1.82% (95% confidence interval [CI]: 1.64, 2.01), 1.02% (95% CI: 0.91, 1.13), 0.69% (95% CI: 0.62, 0.77), and 1.79% (95% CI: 1.60, 1.99) increase in all-cause mortality, respectively. No threshold was observed in the exposure-response associations. Furthermore, significant multiplicative and additive interactions were identified between infiltrated PM and O3. Consequently, greater attention should be directed toward indoor air quality, particularly the coordinated management of combined exposure to indoor PM and O3, in order to better protect public health.

Systems doi: 10.3390/systems14060610

Authors: Carmel Mary Martin Donald Campbell Keith Stockman Ishbel Henderson

Background: Patients described as high-need, high-cost (HNHC) represent a subset of individuals with complex multimorbidity whose healthcare trajectories are characterised by recurrent instability and intensive use of acute care services. Concepts such as trajectory disruption, resilience, and complex adaptive behaviour are widely discussed in health systems research, yet linking these ideas to longitudinal patient care remains limited. The PaJR (Patient Journey Record) relational system was designed using principles from complex adaptive systems theory, enabling longitudinal observation of patient trajectories in real-world care. Objective: This study develops a middle-range theory grounded in longitudinal relational monitoring data. Methods: Two datasets (MonashWatch and Irish cohorts) provide empirical grounding through descriptive analysis of signal clustering, distribution, and multi-domain patterns. Monitoring calls capture structured patient-reported signals across multiple domains, including illness, medication, healthcare utilisation, social support, environmental factors, and self-care. Results: Results demonstrate long-tail signal distributions, temporal clustering, and multi-domain instability preceding admission. Alerts frequently occurred in clusters across consecutive monitoring calls 88% of alert calls were part of a consecutive alert sequence, with approximately 64% of alert calls occurring immediately after a previous alert. Alerts were also commonly multi-domain, with approximately 64% involving disturbances across more than one domain simultaneously.Conclusions: Longitudinal relational monitoring reveals instability patterns in patient journeys that are not visible in episodic health-system data. Recognising these instability phases may enable earlier, more adaptive responses for patients with complex healthcare needs and provides empirical grounding for emerging theories of healthcare trajectories within complex adaptive systems. Although grounded in relational monitoring data, the instability–plasticity framework may extend to inform interpretation across physiological and connected health monitoring systems.

ISPRS International Journal of Geo-Information doi: 10.3390/ijgi15060237

Authors: Jianjun Shi Jiayi Sun Wenxin Shan Yongsheng Jia Yingkang Yao Hongsheng Wang

The assessment of tunnel blasting effects traditionally relies on manual inspection and contact measurements, which are subjective, inefficient, and lack comprehensive quantification. To address this, this study proposes a novel closed-loop framework that integrates multi-view 3D reconstruction with intelligent recognition for quantitative blasting evaluation and parameter optimization. Rather than claiming novelty in these basic computer vision algorithms, the novelty of this work lies in their tunnel blasting oriented integration: reconstructed geometry is converted into blasting relevant indicators and then linked to parameter adjustment decisions within a closed-loop workflow. The framework begins with a standardized image acquisition workflow designed for challenging tunnel environments (e.g., dust, uneven light), followed by image enhancement using histogram equalization and bilateral filtering. A key improvement is an enhanced SIFT feature matching strategy, which incorporates a BBF optimized K-D tree and RANSAC to achieve robust correspondence establishment on texture-repetitive rock surfaces. This enables the generation of high-precision 3D models of the tunnel face via Structure from Motion (SfM) and Poisson surface reconstruction. From these models, quantitative indices are automatically extracted: rock mass structural planes are clustered via the ISODATA algorithm, structural traces are delineated using a minimum cost path method, and face flatness is evaluated through curvature analysis. These indices form the basis for intelligent blasting assessment. Crucially, the assessment results are directly fed back to optimize blasting parameters (e.g., adding cut holes, adjusting auxiliary hole spacing). Field application in the Huangtai Tunnel demonstrated that this closed-loop framework significantly improved face flatness (achieving over 50% improvement in the high-curvature area ratio) and contour control. Further verification in the Donghongshan Tunnel showed that the proportion of the sharp feature region decreased from 20.3% to 7.9% after optimization. The proposed framework transitions blasting management from empirical judgment to a data driven, intelligent optimization process, offering a scalable solution for enhancing quality and efficiency in tunnel construction.

Applied Sciences doi: 10.3390/app16115342

Authors: Alberto Borrega-Solano Pablo Lopez-Sierra Amalia Campos-Redondo Javier Garcia-Rubio

The present study examined whether offensive play-type indicators in professional basketball reflect broader latent playing-style dimensions and whether play-type usage or efficiency is more strongly associated with competitive success. Data were obtained from the official NBA statistics website and included 6400 games across five seasons (2019–2020 to 2023–2024), comprising 5979 regular-season games and 421 playoff games. For each offensive play type, two indicators were analysed separately: usage percentage and efficiency, operationalised as points per possession (PPP). Principal component analyses were conducted independently for regular-season and playoff data, and for usage and efficiency variables. In addition, linear mixed-effects models were used to examine the relationship between play-type indicators and competitive success while accounting for games nested within teams. Only regular-season efficiency variables showed adequate sampling adequacy for factorial analysis (KMO = 0.774), yielding a four-component solution that explained 58.85% of the total variance. In the mixed-effects models, usage variables were not significantly associated with success, whereas efficiency indicators showed greater explanatory value. Specifically, pick-and-roll ball handler PPP and spot-up PPP emerged as the strongest positive predictors of success, with smaller effects observed for roll-man PPP and cut PPP. The efficiency-only model improved model fit relative to the frequency-only model (marginal R2 = 0.799 vs. 0.755), whereas adding usage variables to efficiency provided only a negligible additional contribution (marginal R2 = 0.803). These findings suggest that, in the NBA, competitive success is more closely related to the effectiveness with which offensive actions are executed than to the relative frequency with which they are used. From an applied perspective, play-type efficiency appears to provide more actionable information than usage-based summaries for performance analysis and tactical decision-making.

Electronics doi: 10.3390/electronics15112306

Authors: Kangning Wang Mingzhong Qiao Bo Wu Siyu Chen

This paper presents a 10 kW outer-rotor field-modulated permanent magnet vernier generator tailored for low-speed direct-drive applications. It employs an outer-rotor Spoke-array configuration, which effectively mitigates the leakage flux between adjacent pole pairs. First, the topology and operating principle of the proposed generator are elaborated. Analytical calculations of key design parameters are then performed to accelerate the modeling process. A systematic parametric sweep is conducted to optimize the motor parameters, based on which a 2D finite element analysis model is established. Comprehensive FEA simulations are carried out to investigate its flux regulation capability, static and dynamic characteristics, and permanent magnet demagnetization risk. The results demonstrate that the Spoke-array permanent magnet array effectively suppresses leakage flux, achieving a volumetric power density of 387.5 kW/m3, and the no-load back electromotive force achieves a peak amplitude of 270 V with a total harmonic distortion as low as 3.7%, which is significantly higher than that of conventional permanent magnet vernier generators. Finally, a 30-slot/23-pole prototype is fabricated and tested. The experimental results show excellent agreement with the simulation predictions, validating the effectiveness of the proposed design.

Symmetry doi: 10.3390/sym18060910

Authors: Olivér Ősz József Garab Máté Hegyháti Balázs Dávid

Disruptions in supply networks have caused many logistical and planning challenges in the last few years. The previous predictability of the shipping times of raw materials changed drastically due to various global issues, which affected many production areas, including the wood industry. This work is motivated by a case study of a Central European plywood production facility, where supply-side disruptions caused difficulties in meeting deadlines for downstream companies of the construction and furniture industry. As a result, the objective of production planners shifted towards mitigating the financial burden caused by cancellation penalties. Three MILP (Mixed-Integer Linear Programming) models and a genetic algorithm were developed to tackle the scheduling of a plywood production plant with raw material shipments and order deadlines. The novelty of the considered problem lies in the flexibility of swapping order deadlines from the same client, which was inspired by the real-life deals of the aforementioned company. The methods were tested on 120 benchmark instances of different sizes generated from real industrial data. The genetic algorithm terminated within 60 s for all instances and found the optimal or best-known solution in 71 of 80 short-horizon instances, while also remaining efficient on larger 30-day cases. As the solution approach is not specific to plywood production, it can be applied to scheduling problems in other fields as well, where similar disruptions can develop, and the production process features are covered by the Multi-Mode Resource-Constrained Project Scheduling Problem class.

Entropy doi: 10.3390/e28060591

Authors: Talia Costa Rodrigues David Santana Alencar Tayroni Alencar Alves Gladstone Alencar Alves Francisco Welington Lima João Antônio Plascak

The phase transition of the majority vote model with inertia has been investigated by means of extensive Monte Carlo simulations on directed Erdös–Rényi networks. Besides the usual average connectivity and local field that adds the opinion of the site itself, an additional term of inertia is considered. The relaxation time of the average opinion state of the network, together with the average opinion state fourth-order Binder cumulant and the corresponding opinion state susceptibility, have been analyzed for several different network sizes and local field and inertia parameter values, for average connectivity of 20 connections. The present results show that the phase transition of this model strongly depends on the inertia parameter, being quite different and richer than previous results of the same model on other regular networks. For inertia parameters between zero and 0.1 the system undergoes a continuous phase transition; for values in the range 0.1 and 0.2 no transition can be seen; for still larger values of inertia up to 0.5 a first-order phase transition takes place; finally, for values larger than 0.5 the dynamics is fully dominated by the inertia, and again no phase transition is observed.

Network doi: 10.3390/network6020033

Authors: Massimiliano Gaffurini Dennis Brandão Emiliano Sisinni Paolo Ferrari

Over the last two decades, the automotive industry has spearheaded a shift toward data-centric manufacturing, where Real-Time Ethernet (RTE) networks defined in IEC61784-2 serve as critical components for ensuring deterministic communication at the Operation Technology level. Although AI-based systems offer significant potential for predictive maintenance and cybersecurity, their effectiveness is currently limited by a lack of structured datasets from real-world industrial environments. Most existing research relies on small-scale simulations or laboratory setups that fail to capture the scale and complexity of actual production. To address this gap, this paper introduces a novel methodology for repurposing network data collected throughout a plant’s lifecycle, specifically during the commissioning and validation phases of RTE networks according to IEC61918. An additional important contribution is the creation of the first multi-plant dataset for real RTE (PROFINET) traffic in the automotive sector, aggregating 300 GB of data from 54,000+ devices across nearly 700 production lines in 17 industrial sites. The work defines standardized methodologies and replicable processes for systematic data acquisition, validation, and labeling to ensure long-term usability for training AI models. Finally, four case studies (focused on performance, maintenance, security, and machine learning) show how this dataset can be used to enhance the reliability of modern smart manufacturing.

Applied Sciences doi: 10.3390/app16115341

Authors: Paweł Hatka Karolina Lenarska Adrian Kliks

One of the most intensively developed areas of wireless telecommunications in recent years is the practical application of reconfigurable intelligent surfaces (RISs). This paper presents the results of experimental research conducted at 5.5 GHz using a 16 × 16 element RIS to verify the accuracy of the Tang, Zheng, and Jeong theoretical models, which describe signal behavior upon reflection from an RIS. In contrast to purely simulation-based papers, this study utilizes different antenna types in a laboratory environment representative of a typical office space. The performance of the models is evaluated across distances of 1 m, 1.5 m, and 2 m through a comprehensive quantitative analysis. The study reports core error metrics, including mean and median root mean square error (RMSE), mean absolute error (MAE), and sum of squared differences (SSD), as well as the Interquartile Range (IQR) to assess modeling stability. Furthermore, a Wilcoxon signed-rank test is employed to statistically compare the modeling accuracy.

Machine Learning and Knowledge Extraction doi: 10.3390/make8060145

Authors: Sarala Ghimire Turgay Celik Martin Gerdes Christian W. Omlin

Reliable time-series forecasting under rapidly changing conditions remains a critical challenge across many domains, particularly in healthcare, where physiological signals are inherently dynamic and uncertain. Blood glucose level prediction exemplifies this challenge, as accurate and timely forecasts are essential for effective diabetes management, yet traditional approaches rely on fixed prediction horizons and single-point estimates, which may yield unreliable decisions under rapidly changing physiological conditions. In this work, we propose a novel approach for adaptive horizon selection, applied to BGL prediction, employing a deep learning model. It employs evidential learning-based uncertainty quantification that decompose uncertainty into epistemic and aleatoric. Each set of models is trained to predict blood glucose levels at different future time steps, each providing both a point prediction and an associated uncertainty measure. At inference time, it dynamically balances predictive accuracy and reliability by selecting the longest horizon whose predicted uncertainty remains below a predefined threshold. This enables confidence-based horizon selection, using longer prediction horizons during stable periods and switching to shorter horizons when uncertainty signals critical glucose events requiring immediate intervention. This uncertainty-aware prediction approach promotes transparency by exposing confidence levels alongside predictions. Applicable to time-series forecasting tasks broadly, the proposed framework demonstrates encouraging potential, and when applied to BGL prediction as a representative clinical case, shows particular promise for supporting glycemic management through calibrated uncertainty estimation, offering a more transparent and interpretable alternative to fixed-horizon models toward trustworthy decision support in diabetes care.

AI doi: 10.3390/ai7060193

Authors: Gali Chelouche-Dwek Peter Fonagy

Background: Adults closest to children, including parents and caregivers, teachers, and therapists, are major determinants of child mental health outcomes. However, access to high-quality psychological training for these groups remains severely limited and inequitable. Artificial intelligence (AI) tools may offer a scalable, accessible, and low-cost route to training delivery. This review aimed to provide the first systematic synthesis of evidence on AI tools used to train caregivers, educators, and therapists/practitioners in psychological approaches relevant to child and adolescent mental health. Methods: A systematic review was conducted in accordance with PRISMA guidelines (PROSPERO: CRD420261336167). Five databases, MEDLINE, PsycINFO, Embase, Web of Science, and ERIC, were searched from inception to March 2026, supplemented by reference hand-searching and forward citation tracking. Studies were eligible if they evaluated an AI-based training tool used with adults in caregiving, educational, or therapeutic roles involving children or adolescents aged 0–18 years, delivered a defined psychological approach, and reported at least one training outcome. Owing to substantial methodological and outcome heterogeneity, findings were synthesised narratively, and meta-analysis was not undertaken. Results: Twenty-four studies from nine countries, published between 2019 and 2026, met inclusion criteria. Studies were grouped into caregiver training (Group A, 5 papers), educator training (Group B, 3 papers), and therapist/practitioner training (Group C, 16 papers). Identified AI modalities included natural language processing (NLP)-based chatbots, generative AI/large language model (LLM) systems, AI-integrated virtual reality (VR), and AI-based feedback and analysis tools. Feasibility and acceptability findings were generally positive across groups. However, the evidence base was limited by pervasive methodological weaknesses, including small samples, with most studies enrolling fewer than 30 participants, reliance on unvalidated self-report outcomes, and the absence of follow-up data beyond one month. Conclusions: AI tools show early promise as scalable approaches to psychological training, particularly for procedural skill acquisition and enhancement of practitioner self-efficacy. However, the current evidence base is insufficient to support claims of effectiveness. A structural credibility–accessibility paradox characterises the field: tools with the strongest controlled evidence are the least scalable, while the most accessible tools have the weakest empirical support. Adequately powered, independent randomised controlled trials (RCTs) using validated outcomes, active comparators, and follow-up extending over multiple months are needed across all three population groups.

Applied Sciences doi: 10.3390/app16115339

Authors: Hyun-Bee Kim Nam-Ju Park Byung-Soo Kim

This study develops a quantitative risk assessment framework that explicitly incorporates site-dependent variability in NATM (New Austrian Tunneling Method) tunnel construction projects. The underlying motivation is that identical risk factors can exhibit substantially different risk levels depending on project-specific site conditions. Conventional risk assessment approaches, which rely primarily on probability and impact ratings, are inherently limited in their ability to capture such variations across different project environments. To address this gap, key site condition factors affecting NATM tunnel construction were systematically identified and integrated into the existing risk assessment framework through a structured scoring and weighting process. Eight site condition factors were selected based on an extensive review of domestic and international literature, underground safety evaluation reports, tunnel design standards, geotechnical information databases, standard cost data, and expert consultation. These factors—Geotechnical Condition, Construction Schedule Float, Construction Budget Contingency, Spoil Bank Location, Likelihood of Civil Petitions, Underground Water Level, Environmental (Noise, Vibration), and Site Accessibility (Traffic Constraints)—were each quantified using a five-level scale ranging from 0.6 (very favorable) to 1.4 (very unfavorable). Subsequently, a composite site condition index was derived by combining the assigned scores with corresponding weights, and this index was incorporated as an adjustment coefficient into the conventional risk scoring system. The results demonstrate that, when the composite site condition index is considered, both the final risk magnitude and management priority vary depending on site-specific conditions, even for identical risk factors. This indicates that the proposed framework provides a more refined representation of actual project environments than traditional probability–impact-based approaches. The model can also serve as an effective decision-support tool for developing risk mitigation strategies tailored to specific site characteristics. Accordingly, the proposed model enhances the accuracy of risk assessment in tunnel projects and facilitates the rational identification of critical risks requiring prioritized management. However, because certain evaluation criteria rely on expert judgment, further validation through diverse real-world case studies and improvements to the objectivity of the evaluation framework remain necessary.

Mathematics doi: 10.3390/math14111846

Authors: Yiheng Pan Yongqi Zhang Fanqin Zeng Peng Li Peng Xia Qiyuan Lu Qiqi Luo

This study proposes an enhanced physics-informed neural network (PINN) framework for predicting seepage fields around deeply buried tunnels with limited field measurements. Hard-constrained boundary enforcement via distance-function trial functions is introduced to exactly satisfy Dirichlet conditions on both the ground surface and tunnel perimeter, and Bayesian optimization automates loss weight tuning to replace costly manual calibration. A systematic evaluation of 15 sensor placement schemes demonstrates that the hydraulic head variance across monitoring points, governed by radial coverage distance, is the primary determinant of prediction accuracy—not the number of sensors or angular density. Remarkably, a strategically designed 12-point configuration outperforms 100 randomly distributed points under the idealized conditions studied, confirming that placement quality can dominate over quantity when physics-informed optimization is applied. Transfer learning experiments across 132 geometric configurations reveal a previously unreported geometric transition zone at D/R ≈ 13–15, where prediction errors exhibit a distinct non-monotonic peak. Finite element benchmarking confirms that this error peak stems from the learning characteristics of PINNs under competing boundary influences rather than from the physical complexity of the problem itself. High-density sampling effectively suppresses this peak error by 32% compared with sparse sampling. These findings establish quantitative sensor deployment guidelines for tunnel seepage monitoring and identify fundamental performance boundaries of physics-informed machine learning under geometry–physics coupling.

Risks doi: 10.3390/risks14060123

Authors: Kushantha Fernando Vajira Manathunga

This paper introduces an HJM-style forward modeling framework for valuing American options. Instead of modeling the dynamics of the underlying asset, we model the maturity-indexed forward drift of the gain process, leading to two no-arbitrage representations of the option value. The first is an additive model, where the American option price equals the current gain plus an integral of forward drifts. This representation embeds the early-exercise premium directly and yields a forward drift characterization of the optimal stopping rule. The second is a multiplicative model that provides an arbitrage-free term structure of option values across maturities via a forward rate, in the spirit of the HJM interest rate theory. While it does not determine the early exercise boundary, it is useful for modeling European option price curves and their evolution. We develop the corresponding drift restrictions, spot consistency conditions, and valuation formulas for both representations and provide numerical examples.

Entropy doi: 10.3390/e28060590

Authors: Gaogao Dong Yili Shen Xinqi Hu Ruijin Du

The evolution of connectivity in quantum networks under decoherence and link degradation is a central problem in quantum information, calling for further understanding of the nature of its transition during structural network degradation. By diluting each link with probability 1−f, we focus on connectivity strength transitions in diluted hierarchical scale-free quantum networks, the (u,v) flowers, which are analytically tractable through two adjustable path-length parameters, u≤v. Incorporating quantum concurrence percolation and comparing it with classical percolation, we analyze the transitions of critical thresholds for various values of f and v from analytical, numerical, and simulation perspectives. The results demonstrate that quantum percolation exhibits consistently lower critical thresholds than classical percolation, even under various topologies and dilution levels. Our work implies that quantum multipath entanglement provides an intrinsic compensatory mechanism against structural degradation and that the hierarchical scale-free topology contributes to the failure resistance and robustness of quantum networks with multipath coupling.

Electronics doi: 10.3390/electronics15112308

Authors: Zeyuan Zhang Cunhao Lu Jian Chen

In mobile robot path planning, the conventional A* algorithm often suffers from redundant node expansion and excessive turning points, whereas the Dynamic Window Approach (DWA) is prone to local optima and deviations from the global path in dynamic environments. To address these issues, this paper proposes a hybrid algorithm, termed A*-GA-DWA, which combines an improved A* algorithm with a GA-optimized DWA method. In the global planning stage, a directional six-neighborhood search strategy, an obstacle-aware adaptive heuristic function, and a turning-point smoothing method are introduced to improve path quality and reduce redundant node expansion. In the local planning stage, genetic algorithm optimization is applied to the DWA evaluation weights to enhance obstacle avoidance adaptability in dynamic environments. In addition, key nodes extracted from the global path are used as sub-goals to strengthen the coordination between global guidance and local replanning. Simulation results on a 30 × 30 map with dynamic obstacles show that, compared with conventional A*-DWA, the proposed method reduces the path length by 14.07% and the navigation execution time by 45.98%; compared with M-A*-DWA, the path length and navigation execution time are further reduced by 0.32% and 21.23%, respectively. Additional experiments on a ROS-based mobile robot platform were conducted to further validate the deployability and obstacle-avoidance capability of the proposed framework. These results provide an effective solution for mobile robot path planning tasks.

Applied Sciences doi: 10.3390/app16115340

Authors: Yanqing Zhao Yongze Ma Chuanchen Wang Yi Hu Sifang Feng

The flexible job shop scheduling problem (FJSP) is a typical combinatorial optimization problem in smart manufacturing. Although existing methods have considered machine competition relationships, they lack explicit structured modeling of machine competition relationships induced by candidate operations and are not systematically integrated across state representation, representation learning, and decision-making processes. To address this, this paper proposes a competition-aware dual-attention deep reinforcement learning method. We construct a dynamic heterogeneous graph representation, where machine competition is modeled as state-dependent edges instantiated via a 3D competition tensor, transforming machine competition relationships into structured information, thereby enhancing the model’s ability to characterize complex resource competition patterns. On this basis, we have designed the Competition-Aware Dual-Attention Network (CADAN), which injects competition information into both the attention computation and representation learning processes via a dual-path mechanism, enabling more expressive modeling of machine competition relationships, and which introduces a head-wise competition bias to capture heterogeneous competition patterns. Furthermore, we have developed an adaptive decision head to refine the scores of candidate actions. Our experimental results demonstrate that the proposed method outperforms classical dispatching rules and achieves competitive or superior performance compared with representative evolutionary and learning-based methods on synthetic datasets, public benchmark datasets, and a real-world industrial machining scenario involving mechanical transmission components.

Axioms doi: 10.3390/axioms15060400

Authors: Yasser Almoteri Ahmed Ghezal

This paper investigates the closed-form solvability and dynamical behavior of a class of nonlinear triangular difference systems with overlapping indices, emphasizing the role of coefficient symmetry and asymmetry in determining the qualitative behavior of the system. A unified analytical framework is developed by transforming the original nonlinear system into equivalent linear or multiplicative difference equations, thereby enabling the derivation of explicit general solutions for various parameter configurations. The results show that the structure of the coefficients plays a fundamental role in determining stability, periodicity, and long-term dynamics. In particular, symmetric configurations tend to produce regular and more structured periodic behavior, whereas asymmetric configurations lead to more irregular oscillatory patterns and increased sensitivity to initial conditions. These theoretical findings are supported by numerical simulations and graphical illustrations, which demonstrate how variations in coefficient values and signs influence the evolution of the system. Finally, an application to discrete survival dynamics is presented, illustrating the capability of the proposed model to describe interacting survival processes under both symmetric and asymmetric parameter regimes, thereby highlighting its potential relevance in the study of applied discrete dynamical systems.

Processes doi: 10.3390/pr14111731

Authors: Xiaohu Yang Yonghui Wu Kui Sun Liqiang Ma Jie Peng Shuyue He Shicheng Li Shiqi Chen

Liquid-phase mineralization of CO2 using coal fly ash (CFA) is an efficient approach to permanent CO2 sequestration. To address the low leaching efficiency of calcium ions (Ca2+) in carbon mineralization, this study systematically investigates the leaching performance and leaching mechanism of calcium ions from CFA by using a sequential alkaline-acid processing (i.e., alkaline activation followed by acid leaching). The effects of NaOH concentration, acid concentration, acid type (HCl/CH3COOH), reaction time, and grinding duration on leaching efficiency are studied. The reaction products are characterized by X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). A kinetic model is proposed to analyze the reaction dynamics and leaching mechanisms. The results show that the maximum Ca2+ leaching efficiency for untreated CFA is 43.7% after 40-min acid leaching with 7 mol/L HCl and 1:1.5 S/L ratio. The leaching efficiency can be enhanced to 72.1% after 50-min alkaline activation with 11 mol/L NaOH. Grinding the CFA can further increase the leaching performance of Ca2+. It is shown that the leaching efficiency can be enhanced to 58.75% and 82.3% after 90-min grinding, respectively, for cases without and with 50-min alkaline activation using 9 mol/L NaOH. It is also shown that a peak leaching efficiency of 86.51% can be obtained when 8 mol/L CH3COOH is used for the acid system. The mechanism for the enhancement of leaching efficiency is that both NaOH activation and mechanical grinding can break down the calcium and aluminum silicate vitreous matrix of CFA, facilitating calcium release. Ca2+ leaching performance exhibits two regimes. The leaching efficiency is significantly time-dependent in the first regime, and it remains almost constant in the second regime after the efficiency reaches a pseudo-maximum value. The contribution of this study is that a theoretical foundation is provided for enhancing the Ca2+ recovery from CFA, which makes it practical for large-scale CFA utilization and permanent CO2 sequestration in industry applications.

Applied Sciences doi: 10.3390/app16115338

Authors: Lucia Arnau Muñoz José Vicente Berná Martínez Carlos Calatayud Asensi David Saavedra Pastor

This paper presents the Data Quality Assurance Model (DQAM), a formal model and multilayered architecture designed to guarantee data integrity and robustness in Reliable and Quality-Assured Information Systems. Recognising that inaccurate or corrupted sensor data can lead to system collapses and false alarms in critical services, the DQAM provides a standardised and systematic flow of actions to ensure data excellence for Artificial Intelligence (AI). The architecture is structured into three specialised layers (Acquisition, Processing, and AI Adequacy), implementing formal transformation functions that act as a rigorous filter against data degradation. A core contribution is the mapping of these functions to ISO/IEC 25012 and 5259-2 standards, providing a practical framework for reliable information management. It should be noted that quality dimensions regarding timeliness and data volume are outside the scope of this work, as they depend on external data issuers and end-service requirements. The model’s viability is validated through a real-world implementation on a university campus managing millions of data points, demonstrating its capability to optimise performance—achieving a speedup of up to 43%—and prevent service malfunctions. This work bridges the gap between raw IoT streams, and the high-integrity standards required by modern AI-driven applications.

Buildings doi: 10.3390/buildings16112127

Authors: Ahmet Özdemir Hakan Erdoğan Hasan Özkaynak Baki Öztürk Safa Bozkurt Coşkun

In performance-based earthquake engineering (PBEE), fragility curves hold significant importance for a reliable risk assessment of the existing reinforced concrete (RC) structures. Fragility curves require numerous incremental nonlinear dynamic analyses, which are highly time-consuming and computationally intensive. However, predicting the fragility curve parameters of RC structures by a machine learning algorithm could effectively reduce this cost. In this study, machine learning (ML)-based numerical analyses were performed in order to predict the fragility curve parameters of the existing RC structures, considering rapidly observable structural parameters by street survey. The construction date, story number, plan irregularities, soft story, and damage states are the main variables that are considered in this study. Hence, a dataset comprising the results of 620 structural fragility analyses was compiled from the existing literature. Key fragility parameters, namely the median and standard deviation, are predicted using several machine learning algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs). The performance of the proposed models is evaluated using R2, RMSE, and MAE metrics under a five-fold cross-validation scheme. Furthermore, nonlinear dynamic analyses are conducted on a representative set of structural models to validate the machine learning predictions. The results indicate that the ANN model achieves the highest predictive accuracy, followed by ensemble tree-based methods, demonstrating the capability of machine learning approaches to effectively capture complex nonlinear relationships between seismic input parameters and structural response. The proposed framework significantly reduces computational effort while maintaining reliable prediction accuracy, offering an efficient tool for seismic risk assessment and fragility estimation of existing structures.

Electronics doi: 10.3390/electronics15112307

Authors: Kazım Kıvanç Eren Kerem Küçük Radhwan A. A. Saleh Mehmet Zeki Konyar Olympia M. Hardy Sajjad Ahmad Khan

The rapid expansion of Internet of Things (IoT) technologies has highlighted the need for reliable intrusion detection systems (IDSs), yet the majority of existing studies rely on single-dataset evaluations, raising concerns about their real-world generalisation capability. This study addresses this limitation by systematically investigating distributional shift across heterogeneous IoT intrusion detection datasets and their impact on model behaviour. To achieve this, a unified feature space is constructed using BoT-IoT, ToN-IoT, and UNSW-NB15 datasets, followed by a comprehensive preprocessing pipeline including attack class alignment, distribution-preserving sampling for class imbalance, and feature selection based on cross-dataset feature value propagation analysis. Furthermore, feature-specific transformations and correlation-based dimensionality reduction are applied to enhance statistical consistency and model stability. To simulate realistic deployment scenarios, models are trained on combinations of datasets and evaluated on unseen datasets. The results reveal that distributional inconsistencies and dataset-specific feature biases significantly degrade cross-dataset performance, despite strong within-dataset results. The proposed framework provides a systematic understanding of feature-level behaviour across datasets, identifying both stable and bias-prone features. These findings highlight the necessity of distribution-aware preprocessing and feature analysis for developing robust and generalisable IoT intrusion detection systems.

Applied Sciences doi: 10.3390/app16115337

Authors: Martina Giusti Patrizio Zanobini Domenico Spanò Marco Grosso Maria Pisano Laura Terzo Niccolò Persiani Cosimo Nardi

The use of artificial intelligence (AI) in the radiological field has been extensively investigated from the radiologists’ perspective. Existing studies have primarily focused on AI’s contribution to diagnostic processes and on how its introduction has transformed—and continues to transform—radiologists’ professional practice. The perspectives of radiographers remain underrepresented in the literature, despite their central role in image acquisition and their position as the primary “on-the-ground” operators and managers of imaging technologies. The objective of this study was to analyze the perceptions, attitudes, and expectations of Italian radiographers regarding the introduction of AI, and to provide insights to inform professional training and organizational strategies within healthcare systems. A cross-sectional survey study with qualitative enhancement was adopted as the study design. A survey was administered to a convenience sample, comprising 222 respondents. The findings reveal a high level of familiarity with AI in everyday life, accompanied by an almost complete absence of cultural resistance, suggesting a workforce that is both receptive and ready to evolve. Nevertheless, this individual readiness is contrasted with a substantial institutional and operational gap, characterized by the lack of standardized protocols, regulatory uncertainty, and an uneven distribution of technological resources. The effective integration of AI therefore requires a comprehensive and coordinated approach. Educational reform is necessary to integrate AI and radiomics into university curricula and continuing professional development programs, encompassing not only technical competencies but also ethical, deontological and communication skills. Finally, national and European regulatory frameworks must evolve to clearly define radiographers’ responsibilities within AI-assisted workflows, to establish robust guidelines for data governance and the management of algorithmic outputs.

Buildings doi: 10.3390/buildings16112126

Authors: Qi-Ling Luo Cang Chen Ming-Chao Li Gan-Lin Feng Gao-Xiang Tang

Low-strain reflection testing is widely used for the rapid screening of pile integrity, but its interpretation still relies heavily on manual judgment. This study proposes a dual representation learning framework for classifying the integrity of building foundation piles from low-strain testing records. A dataset containing 1139 piles from engineering projects was established and divided into four integrity classes. Each record was represented in two complementary forms: structured features extracted from engineering parameters and waveform characteristics, and a redrawn waveform image generated from coordinate point data. Support vector machine (SVM), random forest (RF), and convolutional neural network (CNN) models were used as single modality baselines, and their performance was compared with that of a multimodal neural network (MNN) trained on paired structured and image inputs. The multimodal model achieved the highest overall accuracy on the main evaluation subset, reaching 84.65%, whereas the random forest achieved the best Macro-Recall and Macro-F1. This result suggests that multimodal fusion mainly improved overall robustness rather than consistently enhancing performance across all classes. Clearly intact piles and severely defective piles were easier to identify, whereas Class II remained the most difficult category because of its borderline signal characteristics. In the supplementary external validation set, the same ranking of model performance was observed, and the multimodal model achieved an accuracy of 85%. These results indicate that the proposed framework has strong potential for computer-assisted screening of building foundation piles.

Mathematics doi: 10.3390/math14111845

Authors: Muhammad Waseem Min Hong

We present a unified GPU-accelerated framework for real-time Smoothed Particle Hydrodynamics (SPH) fluid simulation with two-way rigid body coupling, secondary particle effects, and volumetric rendering, implemented entirely within the Unity game engine. The framework employs a weakly compressible SPH formulation with O(n) count sort-based spatial hashing and introduces a signed distance field (SDF) coupling system that evaluates three representative geometric primitives, sphere, cylinder, and torus, of increasing topological complexity directly on the GPU. Bidirectional force exchange is achieved through lock-free atomic compare-and-swap impulse accumulation, enabling thousands of fluid particles to interact simultaneously with each rigid body without serialization. A GPU stream compaction–based secondary particle system generates and classifies foam, spray, and bubble effects in real time, while a volumetric rendering pipeline samples fluid density into a 3D texture for SDF-composited volume rendering without surface mesh extraction. A conditional kernel dispatch strategy eliminates GPU cycles for disabled subsystems, and dynamic buffer management reduces memory pressure through runtime allocation. The system sustains above 54 frames per second at four million particles on a consumer-grade GPU, with sub-linear frame time scaling and a 1.70× speedup from dynamic buffer allocation over static pre-allocation.

Journal of Imaging doi: 10.3390/jimaging12060225

Authors: Zili Huang Ran Fan Yongwei Miao

Neural implicit surface representations have yielded impressive results in 3D reconstruction, yet existing methods tend to introduce noise in smooth regions or fail to capture fine details in complex areas, primarily due to a lack of explicit spatial structure modeling. To address these limitations, we propose a geometry-adaptive surface reconstruction method based on Morton codes. By mapping 3D space onto octree traversal paths, this approach provides a natural spatial structural prior for the reconstruction process. For each query point, an implicit octree generates a unique root-to-leaf trajectory, yielding spatially adaptive weights that modulate multi-resolution geometric features. Specifically, low-frequency coarse features dominate in flat regions to suppress noise, whereas high-frequency fine features are activated in edge-rich areas to recover intricate geometry. Experimental results demonstrate competitive performance across multiple datasets, particularly in reconstructing sharp features and fine-grained geometric details.

Symmetry doi: 10.3390/sym18060908

Authors: Aigul Satybaldina Bolatbek Rysbaiuly Aizhan Ydyrys Sultan Alpar Korlan Rysbayeva Auzhan Sakabekov

This study investigates steady-state heat transfer in a three-layer cylindrical system with angular non-uniformity of the temperature field. For the considered geometry, a mathematical model of heat conduction is formulated in cylindrical coordinates with piecewise constant thermophysical properties and continuity conditions at the interfaces between layers. The direct problem is solved analytically using a Fourier series expansion of the temperature field with respect to the angular coordinate. Based on experimental temperature measurements obtained for various configurations of soil layers, an inverse problem is formulated and solved to reconstruct the thermal conductivities of the individual layers and the heat transfer coefficient at the external boundary. To stabilize the solution, a regularized least-squares approach is employed. The convergence of the recovered parameters with respect to the harmonic number is analyzed, and the averaged reconstructed values are compared with the exact parameters used in the direct problem. The obtained results demonstrate the stability and accuracy of the proposed method, confirming its applicability to the identification of thermophysical parameters in multilayer soil systems based on experimental data.

Buildings doi: 10.3390/buildings16112125

Authors: Kai Xiong Junyi Zhao Yao Rong Youhua Zhang Zewen Zhu Chengke Zhang Huijie Zou Yong Yuan

The construction industry is under increasing pressure to reduce its carbon footprint, driving the development of low-carbon construction technologies. Printable concrete has attracted increasing attention in the construction sector due to its advantages in automation and material efficiency, which are considered beneficial for sustainable and low-carbon construction practices. However, its structural performance under fire exposure remains insufficiently understood, particularly regarding the anisotropic mechanical response induced by layer-by-layer fabrication. This study experimentally investigates the mechanical behavior of printable concrete at ambient temperature and after exposure to elevated temperatures of 200 °C, 400 °C, and 600 °C. Manually printed specimens were prepared to replicate the layered characteristics of printable concrete, while cast concrete specimens served as a reference. Results show clear anisotropy in compressive strength among the X, Y, and Z loading directions, with the Z-direction exhibiting the highest strength due to improved interlayer integrity. Compared with cast concrete, printable concrete showed up to 37.77% lower compressive strength at ambient temperature. After thermal exposure, the compressive strength of printable concrete decreased by 16.68%, 34.40%, and 37.54% after exposure to 200 °C, 400 °C, and 600 °C, respectively, while the elastic modulus decreased by up to 78.18%. Mass loss and surface cracking intensified with increasing temperature, reflecting progressive dehydration and microstructural deterioration. These findings provide important insights into the fire performance and post-fire structural assessment of printable concrete.

Electronics doi: 10.3390/electronics15112304

Authors: Kanatbek Bigaliyev Alina Fazylova Kuanysh Alipbayev Ivaylo Stoyanov Bozhana Stoycheva Teodor Iliev

This paper presents an Internet of Things-oriented intelligent supervisory system and high-level control for a small wind turbine powering a residential building. The proposed approach integrates wind generation, battery storage, grid interaction, technical condition analysis, and initial operating mode selection within a single cyber–physical framework. A nonlinear discrete–time hybrid mathematical model was developed for the study, describing the interdependent operating processes of the turbine, storage, and power converter, along with a control algorithm that accounts for constraint flows. A series of experiments are presented for steady-state and dynamic operating scenarios, including wind-speed variations, evening energy shortages, stochastic disturbances, and a developing converter unit fault. As a result, the proposed Internet of Things-oriented supervisory algorithm ensures more efficient utilization of the available wind resource, reduced grid-import dependency, improved battery reserve preservation, and lower thermal loading of the power electronics. Under developing fault conditions and stochastic operating disturbances, the proposed framework maintains more stable residential energy-management behavior and improved operational robustness. The obtained results confirm the potential of the proposed control design for autonomous and semi-autonomous low-power wind energy systems for residential and distributed use.

Journal of Sensor and Actuator Networks doi: 10.3390/jsan15030042

Authors: Rishin Patra Hilary Scott Nkimbeng Cho Jin W. Choi

This paper presents the design of a batteryless near-field communication (NFC) multi-sensor node with an integrated adaptive power-management system for sensing applications. The work focuses on harvesting energy from a 13.56 MHz NFC field to power an ultra-low power sensing platform. The design consists of the TI RF430FRL152H, an integrated NFC transponder with an embedded MSP430 microcontroller core and ferroelectric random-access memory (FRAM) non-volatile memory. The system combines an ISO/IEC 15693 NFC front end, a tuned loop antenna for optimized power harvesting, and multiple analog and digital sensor interfaces, and a firmware architecture for intermittent harvested energy operation. The aforementioned design performs on-demand data acquisition, logs measurements in the FRAM, and communicates the measured results through an ISO15693 compliant NFC link while powered entirely by the reader’s radio-frequency (RF) field. Since NFC provides only limited harvested power, efficient energy management is critical. The proposed scheme continuously monitors the storage capacitor voltage and activates each sensor only when sufficient energy is available. After every measurement, the system reassesses the stored charge before triggering the next acquisition, ensuring stable multi-sensor operation. A BMP390 temperature and pressure sensor and the on-chip temperature sensor demonstrate the platform’s capability. Experimental results show that the system harvests 1.064 mW (1.85 V, 560 µA), achieves a wireless operating range of up to 40 mm, and delivers a response time of 800 ms, demonstrating its suitability for low-power temperature and pressure sensing applications.

Systems doi: 10.3390/systems14060609

Authors: Zhiyong Hao Meiying Xie Xu Zhu Jiawei Liu Xiao Han Linru Zhang Lu Dong Chanjun Liu Junji Cao Zhanfeng Dong Yichen Wang

The analysis of publicly expressed opinions on social media is crucial for designing effective behavioral public policies. By considering both social-media-based public opinion (operationalized as individual, non-representative expressions) and official governmental positions (formal policy statements), this paper employs a systemic case study to understand the political and social factors that influence decision-making in major international events such as Japan’s nuclear wastewater discharge. Using Latent Dirichlet Allocation topic clustering and correlation analysis, this study examines public opinion from five language groups (Chinese, English, Japanese, Korean, and Indonesian, each mapped to a primary country or region: China, the US/UK as representative English-speaking countries, Japan, South Korea, and Indonesia respectively) regarding Japan’s nuclear wastewater discharge, compares governmental attitudes across these five national contexts, and identifies the factors behind their divergence. Public opinion was clustered into six themes; combined with domain expert analysis, they vary significantly across countries that speak different languages in our translated Twitter corpus, though translation artifacts may affect fine-grained comparisons. Public opinion as expressed on Twitter/X is closely associated with a country’s level of international engagement, maritime industry development, and geographic distance from Japan. Furthermore, exploratory analysis of a small set of six countries suggests that governmental positions are influenced more by strategic and economic ties with Japan than by domestic public opinion. Given the small sample size, this finding is preliminary and requires validation in larger-N studies. Public and government opinions on Japan’s nuclear wastewater discharge are sharply divided in the English- and Japanese-language corpora (representing the US/UK and Japan), polarized in the Korean-language corpus (South Korea), and relatively aligned in the Chinese- and Indonesian-language corpora (China and Indonesia). These findings regarding the entire international event system suggest that governments should take public opinion into greater account when addressing international public crises and encourage broader public participation through digital platforms to better respond to global challenges. However, due to the inherent limitations of cross-lingual translation, our cross-country comparisons should be interpreted as indicative rather than definitive.

Electronics doi: 10.3390/electronics15112305

Authors: Chenying Li Xiao Tan Xinyu Huang Ling Sa Nailong Zhang Gang Qiu

Galloping of overhead transmission lines is a low-frequency, large-amplitude vibration hazard that poses a severe threat to power grid safety, yet existing monitoring approaches fail to simultaneously provide flexible deployment, quantitative measurement, and robustness under severe weather conditions. This paper makes three primary contributions. First, we propose a novel line-structure center adsorption algorithm that converts a single operator touch-point into a sub-pixel-precision conductor prompt, achieving prompt accuracy above 95% with one round of interactive correction. Second, we introduce—for the first time—SAM2’s streaming memory architecture for continuous zero-shot pixel-level tracking of galloping conductors under complex outdoor backgrounds including snow, ice, and poor illumination, achieving a segmentation IoU of 93.8% and zero identity switches over 500 consecutive frames, outperforming XMem (87.4%) and DeAOT (88.9%). Third, we develop a two-stage spatial correction framework combining vanishing-point-based inverse perspective mapping (IPM) with equidistant linear transformation (ELT), which eliminates perspective distortion inherent in non-orthogonal field imaging and enables quantitative measurement of galloping amplitude (error < 0.5 m), frequency (error < 0.1 Hz), and inter-phase spacing (ranging error < 1 m). The complete pipeline is implemented on a portable, tripod-mounted device (≤15 kg) integrating a monocular camera, laser rangefinder, and high-precision PTZ gimbal. Field validation at three 110/500 kV sites in Jiangsu Province under extreme winter conditions (−4 °C, Level 5 wind, continuous snowfall) confirms engineering-grade accuracy and practical robustness, providing a viable technical pathway for real-time non-contact galloping monitoring and disaster early warning.

Stats doi: 10.3390/stats9030052

Authors: Fatma Zohra Seghier Halim Zeghdoudi Muhammad Ameeq Sana Kanwal

Modeling over-dispersed count data is a common challenge in applied statistics, especially in engineering applications where repeated events, system faults, and clustered observations often produce variability beyond that allowed by the classical Poisson model. In this paper, we introduce and study the Poisson–QGamma distribution, a new compound discrete model obtained by mixing the Poisson distribution with the QGamma distribution. The proposed distribution is analytically tractable and flexible enough to capture over-dispersion, skewness, and excess kurtosis, which are frequently observed in real count data. Several statistical properties of the distribution are derived, including the probability mass function, cumulative distribution function, survival and hazard rate functions, moments, dispersion index, skewness, kurtosis, entropy, and generating functions. Parameter estimation is considered using maximum likelihood, method of moments, least squares, and weighted least squares methods. The finite-sample behavior of these estimators is examined through Monte Carlo simulation. A regression model based on the Poisson–QGamma distribution is also developed for count responses with covariates. The proposed model is compared with classical and competing count models using simulation and real-data applications. Three engineering-related datasets, involving power grid failure counts, environmental sensor event counts, and packet loss counts in communication networks, are analyzed to illustrate the practical value of the model. The results show that the Poisson–QGamma model provides a better fit than several standard alternatives, including the Poisson, negative binomial, Poisson–Lindley, generalized Poisson, and COM–Poisson models, particularly in the presence of over-dispersion and heavy-tailed behavior. Overall, the proposed distribution offers a parsimonious and effective tool for modeling over-dispersed count data, while also contributing to the broader class of compound discrete distributions.

Symmetry doi: 10.3390/sym18060907

Authors: Youlin Liu Mohammad Faidzul Nasrudin Zainal Rasyid Mahayuddin

3D multi-object tracking (MOT) in complex and dynamic environments remains challenging due to the time-varying reliability of sensor modalities, severe occlusions, and the difficulty of distinguishing instances with similar appearances. Existing methods mainly rely on coarse category-level semantics or heuristic multimodal fusion strategies, which limits fine-grained instance discrimination and leads to unstable trajectory association under complex scenarios. Moreover, current 3D MOT frameworks generally lack the ability to leverage attribute-level semantic information for robust tracking and semantic-aware target retrieval. To address these limitations, we propose AttriMOT, a semantic-aware multimodal 3D MOT framework. Specifically, a category semantic anchoring and competition suppression mechanism is introduced to preserve discriminative fine-grained attribute information among visually similar instances. An attribute-level multimodal alignment module establishes structured correspondences across 3D geometry, 2D appearance, and textual semantics, enabling robust cross-modal representation learning. Furthermore, a parameter-free adaptive confidence fusion strategy dynamically balances LiDAR- and camera-derived trajectory confidence to improve tracking stability under varying environmental conditions. In addition, a semantic-aware trajectory selector is designed to support text-specified target retrieval and trajectory locking, enabling controllable semantic-guided 3D tracking. Extensive experiments on challenging 3D MOT benchmarks demonstrate that AttriMOT consistently outperforms state-of-the-art methods in tracking accuracy and robustness. In particular, AttriMOT achieves 1.33% improvement in HOTA and 0.54% improvement in MOTA compared with the best existing method, while also providing enhanced semantic controllability and text-guided tracking capability.

J doi: 10.3390/j9020016

Authors: Abdul-Muaizz Koray Hamid Rahnema Emmanuel Appiah Kubi Adewale Amosu Oshokoya Gbenga

Waterflooding in oilfields for oil displacement and reservoir pressure maintenance has led to the production of scale in several reservoirs. The formation of scale occurs both in the porous media of the reservoir and in the production equipment, leading to production disruptions that result in a decline in revenue. The aim of this paper is to investigate the effects of mixing samples of seawater and aquifer water. This is achieved by conducting turbidity, salinity, pH, and zeta potential measurements. The risk of self-precipitation of the prepared samples was assessed using the PHREEQC program. A PVT cell was used to assess the impact of temperature and pressure on the prepared seawater and aquifer samples. When 40% of the seawater sample was combined with 60% of the aquifer water sample, the turbidity findings indicated maximum precipitation. The amount of precipitation dropped as temperature and pressure increased. To assess the impact of scale formation on the permeability of a Berea sandstone core, a core flooding experiment was conducted employing liquid and gas as the flowing fluid. Additionally, SEM and EDS analyses were used to examine the shape and composition of scale. It was found that SO42− and Ca2+ ions predominated in scale precipitation.

Applied Sciences doi: 10.3390/app16115334

Authors: Suélen C. Frantz Bruno Melgar Daiana Wischral Guilherme C. da Silva Ricardo C. Calhelha Félix G. de Siqueira Tiane C. Finimundy Priscila Z. Bassinello Lillian Barros

Macrofungi are renowned for their rich nutritional and bioactive compounds. This study aimed to assess bioactive compounds, amino acids, and functional properties of flours produced through solid-state fermentation of bean and rice co-products with macrofungi. Three species (Pycnoporus sanguineus, Fistulina hepatica, and Laetiporus cincinnatus) were cultivated in humidified and sterilized broken ‘carioca’ beans or in a mixture of broken beans (70%), rice bran (20%) and broken rice (10%). Following fermentation, the colonized biomass was dried and milled into flour. The sample derived from broken beans cultivated with F. hepatica (102F) exhibited significantly higher β-glucans content (50.75 mg/g) of flour. All fermented flour samples showed elevated essential amino acid levels surpassing those reported in the literature for carioca beans. Phenolic compounds exhibited a notable increase, exceeding threefold in total phenolic content in the fermented samples. Sample 102F particularly excelled in antioxidant and cytotoxic activities. Principal component analysis revealed that these properties were linked to the highest content of β-glucans and specific phenolic compounds, such as sinapic and ellagic acids. These findings indicate that solid-state fermentation effectively enhances the nutritional and bioactive profile of bean and rice co-products, with F. hepatica emerging as the most promising treatment for bean and the bean–rice mixture.

Applied Sciences doi: 10.3390/app16115330

Authors: Benjamin Higgins John Hewitt Faham Tahmasebinia Christopher Iannuzzi Andrew Peng Krzysztof Skrzypkowski

Dowel-laminated timber (DLT) is a composite structural material manufactured entirely from wood. Increasing awareness of the sustainability, end-of-life recyclability, and potential health concerns associated with synthetic adhesives used in cross-laminated timber (CLT) and glulam has intensified industry and academic interest in adhesive-free mass-timber systems like DLT. In Australia, however, DLT remains under-researched. This paper addresses global and local knowledge gaps by developing a linear-elastic numerical modelling method for DLT using Australian finite element analysis software Strand7 and investigating structural optimisation strategies, including the use of Australian hardwoods. A finite element model captured the characteristic response of a DLT beam from the University of Liverpool within the linear-elastic range. Reduced dowel spacing, alteration of lamella thicknesses and targeted dowel placement in the shear zones increased global stiffness in the parametrisation study. Incorporating Australian hardwood in the outer lamellae further improved bending performance. Structural viability in the Australian context was indicated through the design of a project-scale DLT beam prototype assessed to relevant Australian Standards. The modelling approach and findings are presented alongside a discussion of behavioural nuances, contributing to the growing body of research on DLT.

Applied Sciences doi: 10.3390/app16115336

Authors: Camilo Carrillo González Eloy Díaz Dorado Adrián Juan Pérez Peña José Cidrás Pidre Cristina Isabel Martínez Castañeda José Florencio Sánchez Rúa

Metal forming processes, characterized by high energy consumption, are widely used in modern manufacturing. In this context, methods for monitoring the operational state and cycle-dependent metrics of manufactured parts are essential for implementing energy optimization strategies. Such strategies require moving away from time-aggregated energy assessments, which fail to capture part-level variability, toward analyses at the granularity of individual parts. This article introduces a non-intrusive methodology to enable the identification, in real time, of the part under production and to estimate cycle time and energy consumption per part. The method relies on electrical measurements taken at the switchboards. The RMS current and power values are the inputs to a machine-learning (ML) approach that identifies the part in production. To this end, the time-domain and time–frequency-domain features extracted from the signals are employed to train a Support Vector Machine (SVM) classifier that achieves a test accuracy of 99.9%. Next, the approach estimates cycle time and energy per cycle in real time. Approximately 58,000 production cycles, corresponding to several part types, were characterized. The proposed approach demonstrates that part-level identification and per-cycle energy estimation can be achieved in real time using only electrical measurements in an industrial process.

Applied Sciences doi: 10.3390/app16115335

Authors: Erika Nosková Anna Reitznerová Lýdia Mesarčová Boris Semjon Jozef Nagy Slavomír Marcinčák

The use of plant extracts in the production of fermented meat products can help protect fats from oxidation, improve color stability, and extend their shelf life. The study evaluated the effect of natural extracts (acerola—A, rosemary—R, and their combination—M) on the quality of Tokaj salami stored in MAP at 4 °C for 35 days, compared to negative (N) and positive (K, sodium erythorbate) controls. While the initial chemical composition showed differences due to raw material variability (p < 0.001), fat and protein content remained stable during storage (p > 0.05). In contrast, acidity and water activity (aw) were significantly affected (p < 0.001). Regarding oxidative stability, plant extracts significantly inhibited lipid oxidation during storage (p < 0.05). By day 35, the negative control reached the highest malondialdehyde (MDA) level of 0.67 mg/kg, whereas samples with acerola (A) maintained the lowest values at 0.38 mg/kg, performing comparably to the synthetic antioxidant (0.43 mg/kg; p > 0.05). Acerola extract (A) demonstrated the highest efficacy in stabilizing oxidative changes, with results comparable to the synthetic antioxidant (p > 0.05). Colorimetric analysis revealed that lightness (L*) ranged from 45.98 to 49.75, with L*, a*, and b* parameters significantly influenced by both the antioxidant type and storage phase (p < 0.001). Sensory evaluation remained unaffected by the antioxidants, being affected only by storage time (p < 0.05). These results confirm that acerola and rosemary extracts are viable natural alternatives to sodium erythorbate for maintaining the oxidative and color stability of fermented salami.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060385

Authors: Dilmi C. W. Hettiachchi-Halpe-Kankanamalage Abootaleb Shirvani Nicholas Appiah Svetlozar T. Rachev W. Brent Lindquist Frank J. Fabozzi

We employ an empirical framework for real estate securities that incorporates portfolio optimization, return distribution tail diagnostics, risk metrics, modeling of long-range dependence in return volatility, regression against benchmark indices, and option pricing, treating these as necessary layers of a risk-management structure that concentrates on downside risk. Optimization compared mean–variance against downside-sensitive conditional value at risk. Tail behavior was assessed via skewness, kurtosis, and extreme value theory; volatility persistence was examined using ARMA–FIGARCH models. Benchmark dependence was examined via the capital asset pricing model (CAPM), employing endogenous and exogenous market proxies. Insurance instruments via European options were priced using a doubly subordinated normal inverse Gaussian pricing model capable of modeling skewed, heavy-tailed return distributions. Significant findings for the optimized portfolios include return distributions with losses that are heavier-tailed than gains; a transition in time from moderate-to-high long-range dependence in conditional volatility; smaller values of CAPM “alpha” and “beta” for minimum-risk portfolios compared to tangent portfolios; and significant implied volatility values.

AI doi: 10.3390/ai7060192

Authors: Hugo Martínez Ángeles Cesar Augusto Navarro Rubio José Gabriel Ríos Moreno Margarita G. Garcia-Barajas Roberto Valentín Carrillo-Serrano Mariano Garduño Aparicio José Luis Reyes Araiza Mario Trejo Perea

This study presents a systematic review of Artificial Intelligence (AI) in vehicular bridge engineering, covering design, monitoring, and lifecycle decision support. The objective is to identify, classify, and critically analyze the main AI methods applied across the bridge lifecycle, including Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANNs), and Optimization Algorithms (OAs). The review follows the PRISMA 2020 framework to ensure transparency and reproducibility, considering publications from 2018 to 2026. The results show that AI applications span the entire bridge lifecycle; however, current research is predominantly concentrated in Structural Health Monitoring (SHM), damage detection, inspection, and predictive maintenance, while design-oriented applications—such as optimization, surrogate modeling, and structural analysis—remain comparatively less developed. Importantly, SHM data serve as a key input for data-driven modeling, enabling design optimization, reliability assessment, and lifecycle decision support. Classical ML methods remain effective for structured datasets, whereas DL models, particularly convolutional and recurrent neural networks, dominate image-based and time-series applications. In addition, hybrid physics-informed AI approaches are emerging to improve model reliability and interpretability. The review also identifies key challenges, including data quality limitations, lack of standardized methodologies, limited integration with engineering design codes, and barriers related to trust, expertise, and regulatory frameworks. Overall, the findings highlight a shift toward integrated digital frameworks, including digital twins and multimodal data fusion, to support more reliable monitoring and lifecycle decision-making. This study provides a comprehensive synthesis of current developments and outlines future research directions toward more resilient and intelligent bridge infrastructure systems.